Overview

Brought to you by YData

Dataset statistics

Number of variables264
Number of observations1926393
Missing cells380494779
Missing cells (%)74.8%
Total size in memory3.8 GiB
Average record size in memory2.1 KiB

Variable types

Numeric30
Unsupported112
Text116
Boolean6

Dataset

DescriptionInvertebrate Zoology NMNH Extant Specimen Records 0052489-241126133413365
URLhttps://doi.org/10.15468/dl.fya67r

Alerts

license has constant value "CC0_1_0" Constant
publisher has constant value "National Museum of Natural History, Smithsonian Institution" Constant
institutionID has constant value "urn:lsid:biocol.org:col:34871" Constant
collectionID has constant value "urn:uuid:f14c21a9-8cbf-4c8b-817f-d19d427e2dd6" Constant
institutionCode has constant value "USNM" Constant
collectionCode has constant value "IZ" Constant
datasetName has constant value "NMNH Extant Biology" Constant
materialSampleID has constant value "NORTH_AMERICA" Constant
eventID has constant value "North Pacific Ocean, Gulf Of California" Constant
samplingEffort has constant value "24.1667" Constant
fieldNotes has constant value "-110.283" Constant
georeferencedDate has constant value "8.0" Constant
latestEonOrHighestEonothem has constant value "US" Constant
earliestEraOrLowestErathem has constant value "Idaho" Constant
earliestAgeOrLowestStage has constant value "NORTH_AMERICA" Constant
latestAgeOrHighestStage has constant value "North Pacific Ocean, Departure Bay" Constant
bed has constant value "Moultrie" Constant
identificationRemarks has constant value "-83.7685" Constant
acceptedNameUsage has constant value "SPECIES" Constant
parentNameUsage has constant value "GEOLocate" Constant
namePublishedIn has constant value "ACCEPTED" Constant
subgenus has constant value "False" Constant
cultivarEpithet has constant value "108.0" Constant
protocol has constant value "EML" Constant
relativeOrganismQuantity has constant value "821cc27a-e3bb-4bc5-ac34-89ada245069d" Constant
Unnamed: 223 has constant value "1.0" Constant
Unnamed: 224 has constant value "5967481.0" Constant
Unnamed: 231 has constant value "False" Constant
Unnamed: 234 has constant value "1.0" Constant
Unnamed: 246 has constant value "EML" Constant
Unnamed: 248 has constant value "2024-12-02T11:48:23.416Z" Constant
Unnamed: 252 has constant value "False" Constant
Unnamed: 253 has constant value "NORTH_AMERICA" Constant
Unnamed: 254 has constant value "NORTH_AMERICA" Constant
Unnamed: 255 has constant value "USA" Constant
Unnamed: 256 has constant value "United States" Constant
Unnamed: 257 has constant value "USA.11_1" Constant
Unnamed: 258 has constant value "Georgia" Constant
Unnamed: 259 has constant value "USA.11.35_1" Constant
Unnamed: 260 has constant value "Colquitt" Constant
accessRights has 1926393 (100.0%) missing values Missing
bibliographicCitation has 1926393 (100.0%) missing values Missing
language has 1926393 (100.0%) missing values Missing
references has 1926393 (100.0%) missing values Missing
rightsHolder has 1926393 (100.0%) missing values Missing
type has 1926393 (100.0%) missing values Missing
datasetID has 1926393 (100.0%) missing values Missing
ownerInstitutionCode has 1926393 (100.0%) missing values Missing
informationWithheld has 1926393 (100.0%) missing values Missing
dataGeneralizations has 1926393 (100.0%) missing values Missing
dynamicProperties has 1926393 (100.0%) missing values Missing
recordNumber has 1804640 (93.7%) missing values Missing
recordedBy has 764111 (39.7%) missing values Missing
recordedByID has 1926393 (100.0%) missing values Missing
organismQuantity has 1926393 (100.0%) missing values Missing
organismQuantityType has 1926393 (100.0%) missing values Missing
sex has 1802980 (93.6%) missing values Missing
lifeStage has 1888856 (98.1%) missing values Missing
reproductiveCondition has 1926393 (100.0%) missing values Missing
caste has 1926393 (100.0%) missing values Missing
behavior has 1926393 (100.0%) missing values Missing
vitality has 1926393 (100.0%) missing values Missing
establishmentMeans has 1926393 (100.0%) missing values Missing
degreeOfEstablishment has 1926393 (100.0%) missing values Missing
pathway has 1926393 (100.0%) missing values Missing
georeferenceVerificationStatus has 1926393 (100.0%) missing values Missing
disposition has 1926391 (> 99.9%) missing values Missing
associatedOccurrences has 1926391 (> 99.9%) missing values Missing
associatedReferences has 1926391 (> 99.9%) missing values Missing
associatedSequences has 1921269 (99.7%) missing values Missing
associatedTaxa has 1926391 (> 99.9%) missing values Missing
otherCatalogNumbers has 1926393 (100.0%) missing values Missing
occurrenceRemarks has 1144485 (59.4%) missing values Missing
organismID has 1926393 (100.0%) missing values Missing
organismName has 1926393 (100.0%) missing values Missing
organismScope has 1926393 (100.0%) missing values Missing
associatedOrganisms has 1926393 (100.0%) missing values Missing
previousIdentifications has 1926393 (100.0%) missing values Missing
organismRemarks has 1926393 (100.0%) missing values Missing
materialEntityID has 1926393 (100.0%) missing values Missing
materialEntityRemarks has 1926393 (100.0%) missing values Missing
verbatimLabel has 1926391 (> 99.9%) missing values Missing
materialSampleID has 1926391 (> 99.9%) missing values Missing
eventID has 1926392 (> 99.9%) missing values Missing
parentEventID has 1926393 (100.0%) missing values Missing
eventType has 1926393 (100.0%) missing values Missing
fieldNumber has 1339759 (69.5%) missing values Missing
eventDate has 688611 (35.7%) missing values Missing
eventTime has 1926393 (100.0%) missing values Missing
startDayOfYear has 842313 (43.7%) missing values Missing
endDayOfYear has 842311 (43.7%) missing values Missing
year has 689273 (35.8%) missing values Missing
month has 800939 (41.6%) missing values Missing
day has 887053 (46.0%) missing values Missing
verbatimEventDate has 1173199 (60.9%) missing values Missing
habitat has 1857136 (96.4%) missing values Missing
samplingProtocol has 1926393 (100.0%) missing values Missing
sampleSizeValue has 1926393 (100.0%) missing values Missing
sampleSizeUnit has 1926393 (100.0%) missing values Missing
samplingEffort has 1926392 (> 99.9%) missing values Missing
fieldNotes has 1926392 (> 99.9%) missing values Missing
eventRemarks has 1926393 (100.0%) missing values Missing
locationID has 984066 (51.1%) missing values Missing
higherGeographyID has 1926393 (100.0%) missing values Missing
higherGeography has 67831 (3.5%) missing values Missing
continent has 1027391 (53.3%) missing values Missing
waterBody has 666651 (34.6%) missing values Missing
islandGroup has 1925623 (> 99.9%) missing values Missing
island has 1925415 (99.9%) missing values Missing
countryCode has 110759 (5.7%) missing values Missing
stateProvince has 943673 (49.0%) missing values Missing
county has 1786420 (92.7%) missing values Missing
municipality has 1926393 (100.0%) missing values Missing
locality has 642386 (33.3%) missing values Missing
verbatimLocality has 1926393 (100.0%) missing values Missing
verbatimElevation has 1925931 (> 99.9%) missing values Missing
verticalDatum has 1926393 (100.0%) missing values Missing
verbatimDepth has 1900149 (98.6%) missing values Missing
minimumDistanceAboveSurfaceInMeters has 1926393 (100.0%) missing values Missing
maximumDistanceAboveSurfaceInMeters has 1926393 (100.0%) missing values Missing
locationAccordingTo has 1926393 (100.0%) missing values Missing
locationRemarks has 1926393 (100.0%) missing values Missing
decimalLatitude has 927346 (48.1%) missing values Missing
decimalLongitude has 927346 (48.1%) missing values Missing
coordinateUncertaintyInMeters has 1926393 (100.0%) missing values Missing
coordinatePrecision has 1926393 (100.0%) missing values Missing
pointRadiusSpatialFit has 1926393 (100.0%) missing values Missing
verbatimCoordinateSystem has 1246885 (64.7%) missing values Missing
verbatimSRS has 1926391 (> 99.9%) missing values Missing
footprintWKT has 1926393 (100.0%) missing values Missing
footprintSRS has 1926391 (> 99.9%) missing values Missing
footprintSpatialFit has 1926391 (> 99.9%) missing values Missing
georeferencedBy has 1926391 (> 99.9%) missing values Missing
georeferencedDate has 1926391 (> 99.9%) missing values Missing
georeferenceProtocol has 1265790 (65.7%) missing values Missing
georeferenceSources has 1926390 (> 99.9%) missing values Missing
georeferenceRemarks has 1896105 (98.4%) missing values Missing
geologicalContextID has 1926393 (100.0%) missing values Missing
earliestEonOrLowestEonothem has 1926393 (100.0%) missing values Missing
latestEonOrHighestEonothem has 1926392 (> 99.9%) missing values Missing
earliestEraOrLowestErathem has 1926392 (> 99.9%) missing values Missing
latestEraOrHighestErathem has 1926393 (100.0%) missing values Missing
earliestPeriodOrLowestSystem has 1926393 (100.0%) missing values Missing
latestPeriodOrHighestSystem has 1926393 (100.0%) missing values Missing
earliestEpochOrLowestSeries has 1926391 (> 99.9%) missing values Missing
latestEpochOrHighestSeries has 1926390 (> 99.9%) missing values Missing
earliestAgeOrLowestStage has 1926390 (> 99.9%) missing values Missing
latestAgeOrHighestStage has 1926392 (> 99.9%) missing values Missing
lowestBiostratigraphicZone has 1926393 (100.0%) missing values Missing
highestBiostratigraphicZone has 1926393 (100.0%) missing values Missing
lithostratigraphicTerms has 1926388 (> 99.9%) missing values Missing
group has 1926391 (> 99.9%) missing values Missing
formation has 1926393 (100.0%) missing values Missing
member has 1926393 (100.0%) missing values Missing
bed has 1926392 (> 99.9%) missing values Missing
identificationID has 1926393 (100.0%) missing values Missing
verbatimIdentification has 1926393 (100.0%) missing values Missing
identificationQualifier has 1908260 (99.1%) missing values Missing
typeStatus has 1841066 (95.6%) missing values Missing
identifiedBy has 1085208 (56.3%) missing values Missing
identifiedByID has 1926391 (> 99.9%) missing values Missing
dateIdentified has 1926391 (> 99.9%) missing values Missing
identificationReferences has 1926393 (100.0%) missing values Missing
identificationVerificationStatus has 1926390 (> 99.9%) missing values Missing
identificationRemarks has 1926392 (> 99.9%) missing values Missing
taxonID has 1926393 (100.0%) missing values Missing
scientificNameID has 1926393 (100.0%) missing values Missing
parentNameUsageID has 1926391 (> 99.9%) missing values Missing
originalNameUsageID has 1926393 (100.0%) missing values Missing
nameAccordingToID has 1926393 (100.0%) missing values Missing
namePublishedInID has 1926391 (> 99.9%) missing values Missing
taxonConceptID has 1926393 (100.0%) missing values Missing
acceptedNameUsage has 1926391 (> 99.9%) missing values Missing
parentNameUsage has 1926392 (> 99.9%) missing values Missing
originalNameUsage has 1926393 (100.0%) missing values Missing
nameAccordingTo has 1926393 (100.0%) missing values Missing
namePublishedIn has 1926391 (> 99.9%) missing values Missing
namePublishedInYear has 1926393 (100.0%) missing values Missing
class has 66157 (3.4%) missing values Missing
order has 329537 (17.1%) missing values Missing
superfamily has 1926393 (100.0%) missing values Missing
family has 144488 (7.5%) missing values Missing
subfamily has 1926393 (100.0%) missing values Missing
tribe has 1926393 (100.0%) missing values Missing
subtribe has 1926391 (> 99.9%) missing values Missing
genus has 358044 (18.6%) missing values Missing
genericName has 358043 (18.6%) missing values Missing
subgenus has 1926391 (> 99.9%) missing values Missing
infragenericEpithet has 1926391 (> 99.9%) missing values Missing
specificEpithet has 626798 (32.5%) missing values Missing
infraspecificEpithet has 1890289 (98.1%) missing values Missing
cultivarEpithet has 1926391 (> 99.9%) missing values Missing
verbatimTaxonRank has 1926391 (> 99.9%) missing values Missing
vernacularName has 1926391 (> 99.9%) missing values Missing
nomenclaturalCode has 1926389 (> 99.9%) missing values Missing
nomenclaturalStatus has 1926391 (> 99.9%) missing values Missing
taxonRemarks has 1926390 (> 99.9%) missing values Missing
elevation has 1919570 (99.6%) missing values Missing
elevationAccuracy has 1922885 (99.8%) missing values Missing
depth has 1143682 (59.4%) missing values Missing
depthAccuracy has 1205339 (62.6%) missing values Missing
distanceFromCentroidInMeters has 1917545 (99.5%) missing values Missing
mediaType has 1683241 (87.4%) missing values Missing
classKey has 66158 (3.4%) missing values Missing
orderKey has 329533 (17.1%) missing values Missing
familyKey has 144485 (7.5%) missing values Missing
genusKey has 358041 (18.6%) missing values Missing
subgenusKey has 1926388 (> 99.9%) missing values Missing
speciesKey has 626819 (32.5%) missing values Missing
species has 626822 (32.5%) missing values Missing
verbatimScientificName has 353775 (18.4%) missing values Missing
typifiedName has 1926393 (100.0%) missing values Missing
repatriated has 110144 (5.7%) missing values Missing
relativeOrganismQuantity has 1926392 (> 99.9%) missing values Missing
projectId has 1926390 (> 99.9%) missing values Missing
gbifRegion has 115678 (6.0%) missing values Missing
level0Gid has 1691070 (87.8%) missing values Missing
level0Name has 1691070 (87.8%) missing values Missing
level1Gid has 1694638 (88.0%) missing values Missing
level1Name has 1694634 (88.0%) missing values Missing
level2Gid has 1708984 (88.7%) missing values Missing
level2Name has 1709049 (88.7%) missing values Missing
level3Gid has 1886622 (97.9%) missing values Missing
level3Name has 1887342 (98.0%) missing values Missing
iucnRedListCategory has 469562 (24.4%) missing values Missing
Unnamed: 223 has 1926392 (> 99.9%) missing values Missing
Unnamed: 224 has 1926392 (> 99.9%) missing values Missing
Unnamed: 225 has 1926393 (100.0%) missing values Missing
Unnamed: 226 has 1926393 (100.0%) missing values Missing
Unnamed: 227 has 1926393 (100.0%) missing values Missing
Unnamed: 228 has 1926390 (> 99.9%) missing values Missing
Unnamed: 229 has 1926393 (100.0%) missing values Missing
Unnamed: 230 has 1926390 (> 99.9%) missing values Missing
Unnamed: 231 has 1926390 (> 99.9%) missing values Missing
Unnamed: 232 has 1926389 (> 99.9%) missing values Missing
Unnamed: 233 has 1926390 (> 99.9%) missing values Missing
Unnamed: 234 has 1926390 (> 99.9%) missing values Missing
Unnamed: 235 has 1926389 (> 99.9%) missing values Missing
Unnamed: 236 has 1926389 (> 99.9%) missing values Missing
Unnamed: 237 has 1926389 (> 99.9%) missing values Missing
Unnamed: 238 has 1926389 (> 99.9%) missing values Missing
Unnamed: 239 has 1926390 (> 99.9%) missing values Missing
Unnamed: 240 has 1926393 (100.0%) missing values Missing
Unnamed: 241 has 1926390 (> 99.9%) missing values Missing
Unnamed: 242 has 1926390 (> 99.9%) missing values Missing
Unnamed: 243 has 1926389 (> 99.9%) missing values Missing
Unnamed: 244 has 1926390 (> 99.9%) missing values Missing
Unnamed: 245 has 1926393 (100.0%) missing values Missing
Unnamed: 246 has 1926390 (> 99.9%) missing values Missing
Unnamed: 247 has 1926390 (> 99.9%) missing values Missing
Unnamed: 248 has 1926390 (> 99.9%) missing values Missing
Unnamed: 249 has 1926390 (> 99.9%) missing values Missing
Unnamed: 250 has 1926393 (100.0%) missing values Missing
Unnamed: 251 has 1926393 (100.0%) missing values Missing
Unnamed: 252 has 1926390 (> 99.9%) missing values Missing
Unnamed: 253 has 1926390 (> 99.9%) missing values Missing
Unnamed: 254 has 1926390 (> 99.9%) missing values Missing
Unnamed: 255 has 1926392 (> 99.9%) missing values Missing
Unnamed: 256 has 1926392 (> 99.9%) missing values Missing
Unnamed: 257 has 1926392 (> 99.9%) missing values Missing
Unnamed: 258 has 1926392 (> 99.9%) missing values Missing
Unnamed: 259 has 1926392 (> 99.9%) missing values Missing
Unnamed: 260 has 1926392 (> 99.9%) missing values Missing
Unnamed: 261 has 1926393 (100.0%) missing values Missing
Unnamed: 262 has 1926393 (100.0%) missing values Missing
individualCount is highly skewed (γ1 = 100.3828675) Skewed
gbifID has unique values Unique
occurrenceID has unique values Unique
accessRights is an unsupported type, check if it needs cleaning or further analysis Unsupported
bibliographicCitation is an unsupported type, check if it needs cleaning or further analysis Unsupported
language is an unsupported type, check if it needs cleaning or further analysis Unsupported
references is an unsupported type, check if it needs cleaning or further analysis Unsupported
rightsHolder is an unsupported type, check if it needs cleaning or further analysis Unsupported
type is an unsupported type, check if it needs cleaning or further analysis Unsupported
datasetID is an unsupported type, check if it needs cleaning or further analysis Unsupported
ownerInstitutionCode is an unsupported type, check if it needs cleaning or further analysis Unsupported
informationWithheld is an unsupported type, check if it needs cleaning or further analysis Unsupported
dataGeneralizations is an unsupported type, check if it needs cleaning or further analysis Unsupported
dynamicProperties is an unsupported type, check if it needs cleaning or further analysis Unsupported
recordedByID is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismQuantity is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismQuantityType is an unsupported type, check if it needs cleaning or further analysis Unsupported
reproductiveCondition is an unsupported type, check if it needs cleaning or further analysis Unsupported
caste is an unsupported type, check if it needs cleaning or further analysis Unsupported
behavior is an unsupported type, check if it needs cleaning or further analysis Unsupported
vitality is an unsupported type, check if it needs cleaning or further analysis Unsupported
establishmentMeans is an unsupported type, check if it needs cleaning or further analysis Unsupported
degreeOfEstablishment is an unsupported type, check if it needs cleaning or further analysis Unsupported
pathway is an unsupported type, check if it needs cleaning or further analysis Unsupported
georeferenceVerificationStatus is an unsupported type, check if it needs cleaning or further analysis Unsupported
otherCatalogNumbers is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismID is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismName is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismScope is an unsupported type, check if it needs cleaning or further analysis Unsupported
associatedOrganisms is an unsupported type, check if it needs cleaning or further analysis Unsupported
previousIdentifications is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
materialEntityID is an unsupported type, check if it needs cleaning or further analysis Unsupported
materialEntityRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
parentEventID is an unsupported type, check if it needs cleaning or further analysis Unsupported
eventType is an unsupported type, check if it needs cleaning or further analysis Unsupported
eventTime is an unsupported type, check if it needs cleaning or further analysis Unsupported
endDayOfYear is an unsupported type, check if it needs cleaning or further analysis Unsupported
samplingProtocol is an unsupported type, check if it needs cleaning or further analysis Unsupported
sampleSizeValue is an unsupported type, check if it needs cleaning or further analysis Unsupported
sampleSizeUnit is an unsupported type, check if it needs cleaning or further analysis Unsupported
eventRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
higherGeographyID is an unsupported type, check if it needs cleaning or further analysis Unsupported
municipality is an unsupported type, check if it needs cleaning or further analysis Unsupported
verbatimLocality is an unsupported type, check if it needs cleaning or further analysis Unsupported
verbatimElevation is an unsupported type, check if it needs cleaning or further analysis Unsupported
verticalDatum is an unsupported type, check if it needs cleaning or further analysis Unsupported
minimumDistanceAboveSurfaceInMeters is an unsupported type, check if it needs cleaning or further analysis Unsupported
maximumDistanceAboveSurfaceInMeters is an unsupported type, check if it needs cleaning or further analysis Unsupported
locationAccordingTo is an unsupported type, check if it needs cleaning or further analysis Unsupported
locationRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
coordinateUncertaintyInMeters is an unsupported type, check if it needs cleaning or further analysis Unsupported
coordinatePrecision is an unsupported type, check if it needs cleaning or further analysis Unsupported
pointRadiusSpatialFit is an unsupported type, check if it needs cleaning or further analysis Unsupported
footprintWKT is an unsupported type, check if it needs cleaning or further analysis Unsupported
geologicalContextID is an unsupported type, check if it needs cleaning or further analysis Unsupported
earliestEonOrLowestEonothem is an unsupported type, check if it needs cleaning or further analysis Unsupported
latestEraOrHighestErathem is an unsupported type, check if it needs cleaning or further analysis Unsupported
earliestPeriodOrLowestSystem is an unsupported type, check if it needs cleaning or further analysis Unsupported
latestPeriodOrHighestSystem is an unsupported type, check if it needs cleaning or further analysis Unsupported
lowestBiostratigraphicZone is an unsupported type, check if it needs cleaning or further analysis Unsupported
highestBiostratigraphicZone is an unsupported type, check if it needs cleaning or further analysis Unsupported
formation is an unsupported type, check if it needs cleaning or further analysis Unsupported
member is an unsupported type, check if it needs cleaning or further analysis Unsupported
identificationID is an unsupported type, check if it needs cleaning or further analysis Unsupported
verbatimIdentification is an unsupported type, check if it needs cleaning or further analysis Unsupported
identificationReferences is an unsupported type, check if it needs cleaning or further analysis Unsupported
identificationVerificationStatus is an unsupported type, check if it needs cleaning or further analysis Unsupported
taxonID is an unsupported type, check if it needs cleaning or further analysis Unsupported
scientificNameID is an unsupported type, check if it needs cleaning or further analysis Unsupported
acceptedNameUsageID is an unsupported type, check if it needs cleaning or further analysis Unsupported
originalNameUsageID is an unsupported type, check if it needs cleaning or further analysis Unsupported
nameAccordingToID is an unsupported type, check if it needs cleaning or further analysis Unsupported
taxonConceptID is an unsupported type, check if it needs cleaning or further analysis Unsupported
originalNameUsage is an unsupported type, check if it needs cleaning or further analysis Unsupported
nameAccordingTo is an unsupported type, check if it needs cleaning or further analysis Unsupported
namePublishedInYear is an unsupported type, check if it needs cleaning or further analysis Unsupported
superfamily is an unsupported type, check if it needs cleaning or further analysis Unsupported
subfamily is an unsupported type, check if it needs cleaning or further analysis Unsupported
tribe is an unsupported type, check if it needs cleaning or further analysis Unsupported
nomenclaturalCode is an unsupported type, check if it needs cleaning or further analysis Unsupported
elevation is an unsupported type, check if it needs cleaning or further analysis Unsupported
elevationAccuracy is an unsupported type, check if it needs cleaning or further analysis Unsupported
depth is an unsupported type, check if it needs cleaning or further analysis Unsupported
depthAccuracy is an unsupported type, check if it needs cleaning or further analysis Unsupported
distanceFromCentroidInMeters is an unsupported type, check if it needs cleaning or further analysis Unsupported
hasCoordinate is an unsupported type, check if it needs cleaning or further analysis Unsupported
hasGeospatialIssues is an unsupported type, check if it needs cleaning or further analysis Unsupported
taxonKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
acceptedTaxonKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
kingdomKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
phylumKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
classKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
orderKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
familyKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
genusKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
speciesKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
typifiedName is an unsupported type, check if it needs cleaning or further analysis Unsupported
isSequenced is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 225 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 226 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 227 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 229 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 232 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 235 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 236 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 237 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 238 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 240 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 241 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 245 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 250 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 251 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 261 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 262 is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-01-07 15:44:29.119919
Analysis finished2025-01-07 15:45:53.765476
Duration1 minute and 24.65 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

gbifID
Real number (ℝ)

Unique 

Distinct1926393
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515389515
Minimum1317202449
Maximum4987328269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:45:53.822644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1317202449
5-th percentile1317561912
Q11318996414
median1320788619
Q31322584569
95-th percentile2571403064
Maximum4987328269
Range3670125820
Interquartile range (IQR)3588155

Descriptive statistics

Standard deviation569129145
Coefficient of variation (CV)0.3755662418
Kurtosis14.89150629
Mean1515389515
Median Absolute Deviation (MAD)1794060
Skewness3.74295434
Sum2.919235754 × 1015
Variance3.239079837 × 1017
MonotonicityNot monotonic
2025-01-07T10:45:53.888375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1318343233 1
 
< 0.1%
1321728981 1
 
< 0.1%
1320179422 1
 
< 0.1%
1320179575 1
 
< 0.1%
1321729723 1
 
< 0.1%
1320179846 1
 
< 0.1%
1321730497 1
 
< 0.1%
1320180949 1
 
< 0.1%
1320181165 1
 
< 0.1%
1318339663 1
 
< 0.1%
Other values (1926383) 1926383
> 99.9%
ValueCountFrequency (%)
1317202449 1
< 0.1%
1317202455 1
< 0.1%
1317202456 1
< 0.1%
1317202459 1
< 0.1%
1317202460 1
< 0.1%
ValueCountFrequency (%)
4987328269 1
< 0.1%
4987328266 1
< 0.1%
4987328256 1
< 0.1%
4987328247 1
< 0.1%
4987328207 1
< 0.1%

accessRights
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

bibliographicCitation
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

language
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

license
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.7 MiB
2025-01-07T10:45:53.931881image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters13484751
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCC0_1_0
2nd rowCC0_1_0
3rd rowCC0_1_0
4th rowCC0_1_0
5th rowCC0_1_0
ValueCountFrequency (%)
cc0_1_0 1926393
100.0%
2025-01-07T10:45:54.022455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 3852786
28.6%
0 3852786
28.6%
_ 3852786
28.6%
1 1926393
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13484751
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 3852786
28.6%
0 3852786
28.6%
_ 3852786
28.6%
1 1926393
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13484751
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 3852786
28.6%
0 3852786
28.6%
_ 3852786
28.6%
1 1926393
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13484751
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 3852786
28.6%
0 3852786
28.6%
_ 3852786
28.6%
1 1926393
14.3%
Distinct113487
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size14.7 MiB
2025-01-07T10:45:54.157495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters38527860
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62369 ?
Unique (%)3.2%

Sample

1st row2021-10-06T15:29:00Z
2nd row2024-09-25T16:08:00Z
3rd row2020-01-06T17:42:00Z
4th row2018-09-17T12:46:00Z
5th row2024-09-25T15:32:00Z
ValueCountFrequency (%)
2024-09-25t13:44:00z 9049
 
0.5%
2024-09-25t13:46:00z 8728
 
0.5%
2024-09-25t17:07:00z 8646
 
0.4%
2024-09-25t17:10:00z 8633
 
0.4%
2024-09-25t17:05:00z 8623
 
0.4%
2024-09-25t13:45:00z 8553
 
0.4%
2024-09-25t17:11:00z 8500
 
0.4%
2024-09-25t17:08:00z 8494
 
0.4%
2024-09-25t15:27:00z 8472
 
0.4%
2024-09-25t17:15:00z 8471
 
0.4%
Other values (113477) 1840224
95.5%
2025-01-07T10:45:54.363062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8971409
23.3%
2 4988502
12.9%
1 4688771
12.2%
- 3852786
10.0%
: 3852786
10.0%
T 1926393
 
5.0%
Z 1926393
 
5.0%
4 1757743
 
4.6%
5 1702088
 
4.4%
9 1536985
 
4.0%
Other values (4) 3324004
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38527860
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8971409
23.3%
2 4988502
12.9%
1 4688771
12.2%
- 3852786
10.0%
: 3852786
10.0%
T 1926393
 
5.0%
Z 1926393
 
5.0%
4 1757743
 
4.6%
5 1702088
 
4.4%
9 1536985
 
4.0%
Other values (4) 3324004
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38527860
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8971409
23.3%
2 4988502
12.9%
1 4688771
12.2%
- 3852786
10.0%
: 3852786
10.0%
T 1926393
 
5.0%
Z 1926393
 
5.0%
4 1757743
 
4.6%
5 1702088
 
4.4%
9 1536985
 
4.0%
Other values (4) 3324004
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38527860
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8971409
23.3%
2 4988502
12.9%
1 4688771
12.2%
- 3852786
10.0%
: 3852786
10.0%
T 1926393
 
5.0%
Z 1926393
 
5.0%
4 1757743
 
4.6%
5 1702088
 
4.4%
9 1536985
 
4.0%
Other values (4) 3324004
 
8.6%

publisher
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.7 MiB
2025-01-07T10:45:54.433995image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length59
Median length59
Mean length59
Min length59

Characters and Unicode

Total characters113657187
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNational Museum of Natural History, Smithsonian Institution
2nd rowNational Museum of Natural History, Smithsonian Institution
3rd rowNational Museum of Natural History, Smithsonian Institution
4th rowNational Museum of Natural History, Smithsonian Institution
5th rowNational Museum of Natural History, Smithsonian Institution
ValueCountFrequency (%)
national 1926393
14.3%
museum 1926393
14.3%
of 1926393
14.3%
natural 1926393
14.3%
history 1926393
14.3%
smithsonian 1926393
14.3%
institution 1926393
14.3%
2025-01-07T10:45:54.550123image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 13484751
11.9%
i 11558358
10.2%
11558358
10.2%
o 9631965
 
8.5%
a 9631965
 
8.5%
n 9631965
 
8.5%
s 7705572
 
6.8%
u 7705572
 
6.8%
N 3852786
 
3.4%
m 3852786
 
3.4%
Other values (11) 25043109
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113657187
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 13484751
11.9%
i 11558358
10.2%
11558358
10.2%
o 9631965
 
8.5%
a 9631965
 
8.5%
n 9631965
 
8.5%
s 7705572
 
6.8%
u 7705572
 
6.8%
N 3852786
 
3.4%
m 3852786
 
3.4%
Other values (11) 25043109
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113657187
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 13484751
11.9%
i 11558358
10.2%
11558358
10.2%
o 9631965
 
8.5%
a 9631965
 
8.5%
n 9631965
 
8.5%
s 7705572
 
6.8%
u 7705572
 
6.8%
N 3852786
 
3.4%
m 3852786
 
3.4%
Other values (11) 25043109
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113657187
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 13484751
11.9%
i 11558358
10.2%
11558358
10.2%
o 9631965
 
8.5%
a 9631965
 
8.5%
n 9631965
 
8.5%
s 7705572
 
6.8%
u 7705572
 
6.8%
N 3852786
 
3.4%
m 3852786
 
3.4%
Other values (11) 25043109
22.0%

references
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

rightsHolder
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

type
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

institutionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.7 MiB
2025-01-07T10:45:54.613538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters55865397
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowurn:lsid:biocol.org:col:34871
2nd rowurn:lsid:biocol.org:col:34871
3rd rowurn:lsid:biocol.org:col:34871
4th rowurn:lsid:biocol.org:col:34871
5th rowurn:lsid:biocol.org:col:34871
ValueCountFrequency (%)
urn:lsid:biocol.org:col:34871 1926393
100.0%
2025-01-07T10:45:54.718335image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 7705572
13.8%
: 7705572
13.8%
l 5779179
 
10.3%
r 3852786
 
6.9%
c 3852786
 
6.9%
i 3852786
 
6.9%
u 1926393
 
3.4%
s 1926393
 
3.4%
d 1926393
 
3.4%
n 1926393
 
3.4%
Other values (8) 15411144
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55865397
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 7705572
13.8%
: 7705572
13.8%
l 5779179
 
10.3%
r 3852786
 
6.9%
c 3852786
 
6.9%
i 3852786
 
6.9%
u 1926393
 
3.4%
s 1926393
 
3.4%
d 1926393
 
3.4%
n 1926393
 
3.4%
Other values (8) 15411144
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55865397
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 7705572
13.8%
: 7705572
13.8%
l 5779179
 
10.3%
r 3852786
 
6.9%
c 3852786
 
6.9%
i 3852786
 
6.9%
u 1926393
 
3.4%
s 1926393
 
3.4%
d 1926393
 
3.4%
n 1926393
 
3.4%
Other values (8) 15411144
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55865397
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 7705572
13.8%
: 7705572
13.8%
l 5779179
 
10.3%
r 3852786
 
6.9%
c 3852786
 
6.9%
i 3852786
 
6.9%
u 1926393
 
3.4%
s 1926393
 
3.4%
d 1926393
 
3.4%
n 1926393
 
3.4%
Other values (8) 15411144
27.6%

collectionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.7 MiB
2025-01-07T10:45:54.775584image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length45
Median length45
Mean length45
Min length45

Characters and Unicode

Total characters86687685
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowurn:uuid:f14c21a9-8cbf-4c8b-817f-d19d427e2dd6
2nd rowurn:uuid:f14c21a9-8cbf-4c8b-817f-d19d427e2dd6
3rd rowurn:uuid:f14c21a9-8cbf-4c8b-817f-d19d427e2dd6
4th rowurn:uuid:f14c21a9-8cbf-4c8b-817f-d19d427e2dd6
5th rowurn:uuid:f14c21a9-8cbf-4c8b-817f-d19d427e2dd6
ValueCountFrequency (%)
urn:uuid:f14c21a9-8cbf-4c8b-817f-d19d427e2dd6 1926393
100.0%
2025-01-07T10:45:54.880290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 9631965
11.1%
1 7705572
 
8.9%
- 7705572
 
8.9%
c 5779179
 
6.7%
2 5779179
 
6.7%
u 5779179
 
6.7%
4 5779179
 
6.7%
8 5779179
 
6.7%
f 5779179
 
6.7%
7 3852786
 
4.4%
Other values (9) 23116716
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86687685
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 9631965
11.1%
1 7705572
 
8.9%
- 7705572
 
8.9%
c 5779179
 
6.7%
2 5779179
 
6.7%
u 5779179
 
6.7%
4 5779179
 
6.7%
8 5779179
 
6.7%
f 5779179
 
6.7%
7 3852786
 
4.4%
Other values (9) 23116716
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86687685
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 9631965
11.1%
1 7705572
 
8.9%
- 7705572
 
8.9%
c 5779179
 
6.7%
2 5779179
 
6.7%
u 5779179
 
6.7%
4 5779179
 
6.7%
8 5779179
 
6.7%
f 5779179
 
6.7%
7 3852786
 
4.4%
Other values (9) 23116716
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86687685
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 9631965
11.1%
1 7705572
 
8.9%
- 7705572
 
8.9%
c 5779179
 
6.7%
2 5779179
 
6.7%
u 5779179
 
6.7%
4 5779179
 
6.7%
8 5779179
 
6.7%
f 5779179
 
6.7%
7 3852786
 
4.4%
Other values (9) 23116716
26.7%

datasetID
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

institutionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.7 MiB
2025-01-07T10:45:54.921640image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters7705572
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSNM
2nd rowUSNM
3rd rowUSNM
4th rowUSNM
5th rowUSNM
ValueCountFrequency (%)
usnm 1926393
100.0%
2025-01-07T10:45:55.009734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 1926393
25.0%
S 1926393
25.0%
N 1926393
25.0%
M 1926393
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7705572
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 1926393
25.0%
S 1926393
25.0%
N 1926393
25.0%
M 1926393
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7705572
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 1926393
25.0%
S 1926393
25.0%
N 1926393
25.0%
M 1926393
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7705572
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 1926393
25.0%
S 1926393
25.0%
N 1926393
25.0%
M 1926393
25.0%

collectionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.7 MiB
2025-01-07T10:45:55.049733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3852786
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIZ
2nd rowIZ
3rd rowIZ
4th rowIZ
5th rowIZ
ValueCountFrequency (%)
iz 1926393
100.0%
2025-01-07T10:45:55.135295image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 1926393
50.0%
Z 1926393
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3852786
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 1926393
50.0%
Z 1926393
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3852786
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 1926393
50.0%
Z 1926393
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3852786
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 1926393
50.0%
Z 1926393
50.0%

datasetName
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.7 MiB
2025-01-07T10:45:55.177295image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters36601467
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNMNH Extant Biology
2nd rowNMNH Extant Biology
3rd rowNMNH Extant Biology
4th rowNMNH Extant Biology
5th rowNMNH Extant Biology
ValueCountFrequency (%)
nmnh 1926393
33.3%
extant 1926393
33.3%
biology 1926393
33.3%
2025-01-07T10:45:55.337404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 3852786
 
10.5%
t 3852786
 
10.5%
3852786
 
10.5%
o 3852786
 
10.5%
H 1926393
 
5.3%
E 1926393
 
5.3%
M 1926393
 
5.3%
x 1926393
 
5.3%
a 1926393
 
5.3%
B 1926393
 
5.3%
Other values (5) 9631965
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36601467
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 3852786
 
10.5%
t 3852786
 
10.5%
3852786
 
10.5%
o 3852786
 
10.5%
H 1926393
 
5.3%
E 1926393
 
5.3%
M 1926393
 
5.3%
x 1926393
 
5.3%
a 1926393
 
5.3%
B 1926393
 
5.3%
Other values (5) 9631965
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36601467
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 3852786
 
10.5%
t 3852786
 
10.5%
3852786
 
10.5%
o 3852786
 
10.5%
H 1926393
 
5.3%
E 1926393
 
5.3%
M 1926393
 
5.3%
x 1926393
 
5.3%
a 1926393
 
5.3%
B 1926393
 
5.3%
Other values (5) 9631965
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36601467
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 3852786
 
10.5%
t 3852786
 
10.5%
3852786
 
10.5%
o 3852786
 
10.5%
H 1926393
 
5.3%
E 1926393
 
5.3%
M 1926393
 
5.3%
x 1926393
 
5.3%
a 1926393
 
5.3%
B 1926393
 
5.3%
Other values (5) 9631965
26.3%

ownerInstitutionCode
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.7 MiB
2025-01-07T10:45:55.388968image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length18
Mean length18.00144052
Min length17

Characters and Unicode

Total characters34677849
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRESERVED_SPECIMEN
2nd rowPRESERVED_SPECIMEN
3rd rowPRESERVED_SPECIMEN
4th rowPRESERVED_SPECIMEN
5th rowPRESERVED_SPECIMEN
ValueCountFrequency (%)
preserved_specimen 1922256
99.8%
machine_observation 3456
 
0.2%
human_observation 681
 
< 0.1%
2025-01-07T10:45:55.503919image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 9618873
27.7%
S 3848649
11.1%
R 3848649
11.1%
P 3844512
 
11.1%
N 1930530
 
5.6%
I 1929849
 
5.6%
V 1926393
 
5.6%
M 1926393
 
5.6%
_ 1926393
 
5.6%
C 1925712
 
5.6%
Other values (7) 1951896
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34677849
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 9618873
27.7%
S 3848649
11.1%
R 3848649
11.1%
P 3844512
 
11.1%
N 1930530
 
5.6%
I 1929849
 
5.6%
V 1926393
 
5.6%
M 1926393
 
5.6%
_ 1926393
 
5.6%
C 1925712
 
5.6%
Other values (7) 1951896
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34677849
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 9618873
27.7%
S 3848649
11.1%
R 3848649
11.1%
P 3844512
 
11.1%
N 1930530
 
5.6%
I 1929849
 
5.6%
V 1926393
 
5.6%
M 1926393
 
5.6%
_ 1926393
 
5.6%
C 1925712
 
5.6%
Other values (7) 1951896
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34677849
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 9618873
27.7%
S 3848649
11.1%
R 3848649
11.1%
P 3844512
 
11.1%
N 1930530
 
5.6%
I 1929849
 
5.6%
V 1926393
 
5.6%
M 1926393
 
5.6%
_ 1926393
 
5.6%
C 1925712
 
5.6%
Other values (7) 1951896
 
5.6%

informationWithheld
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

dataGeneralizations
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

dynamicProperties
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

occurrenceID
Text

Unique 

Distinct1926393
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size14.7 MiB
2025-01-07T10:45:56.448955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length63
Median length63
Mean length63
Min length63

Characters and Unicode

Total characters121362759
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1926393 ?
Unique (%)100.0%

Sample

1st rowhttp://n2t.net/ark:/65665/3c831e8df-8799-47a1-8dcf-bcb0b77fd3e3
2nd rowhttp://n2t.net/ark:/65665/383ab647e-23a7-4086-b71e-36212ccc0eb2
3rd rowhttp://n2t.net/ark:/65665/383adbf6e-f769-4dc3-8bef-550530af49ee
4th rowhttp://n2t.net/ark:/65665/3c83aad38-c935-46fa-96c3-e450ebb169cf
5th rowhttp://n2t.net/ark:/65665/383b126a6-bf3a-4908-bc33-e4435555fcc5
ValueCountFrequency (%)
http://n2t.net/ark:/65665/383cb8e2a-4f46-4138-82be-3d7989851c9e 1
 
< 0.1%
http://n2t.net/ark:/65665/33275786b-f1fe-4add-972f-33ff5c507828 1
 
< 0.1%
http://n2t.net/ark:/65665/3c831e8df-8799-47a1-8dcf-bcb0b77fd3e3 1
 
< 0.1%
http://n2t.net/ark:/65665/383ab647e-23a7-4086-b71e-36212ccc0eb2 1
 
< 0.1%
http://n2t.net/ark:/65665/383adbf6e-f769-4dc3-8bef-550530af49ee 1
 
< 0.1%
http://n2t.net/ark:/65665/3c83aad38-c935-46fa-96c3-e450ebb169cf 1
 
< 0.1%
http://n2t.net/ark:/65665/383b126a6-bf3a-4908-bc33-e4435555fcc5 1
 
< 0.1%
http://n2t.net/ark:/65665/3c843fd56-7874-4858-b938-14fdfcb5544c 1
 
< 0.1%
http://n2t.net/ark:/65665/383bcb698-5477-4feb-9966-d9adae345f09 1
 
< 0.1%
http://n2t.net/ark:/65665/375bb0af4-5d38-4cd6-b8a0-08c73948a463 1
 
< 0.1%
Other values (1926383) 1926383
> 99.9%
2025-01-07T10:45:57.407414image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 9631965
 
7.9%
6 9394397
 
7.7%
- 7705572
 
6.3%
t 7705572
 
6.3%
5 7461179
 
6.1%
a 6018602
 
5.0%
3 5539470
 
4.6%
e 5537694
 
4.6%
2 5537394
 
4.6%
4 5534549
 
4.6%
Other values (16) 51296365
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121362759
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 9631965
 
7.9%
6 9394397
 
7.7%
- 7705572
 
6.3%
t 7705572
 
6.3%
5 7461179
 
6.1%
a 6018602
 
5.0%
3 5539470
 
4.6%
e 5537694
 
4.6%
2 5537394
 
4.6%
4 5534549
 
4.6%
Other values (16) 51296365
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121362759
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 9631965
 
7.9%
6 9394397
 
7.7%
- 7705572
 
6.3%
t 7705572
 
6.3%
5 7461179
 
6.1%
a 6018602
 
5.0%
3 5539470
 
4.6%
e 5537694
 
4.6%
2 5537394
 
4.6%
4 5534549
 
4.6%
Other values (16) 51296365
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121362759
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 9631965
 
7.9%
6 9394397
 
7.7%
- 7705572
 
6.3%
t 7705572
 
6.3%
5 7461179
 
6.1%
a 6018602
 
5.0%
3 5539470
 
4.6%
e 5537694
 
4.6%
2 5537394
 
4.6%
4 5534549
 
4.6%
Other values (16) 51296365
42.3%
Distinct1355393
Distinct (%)70.4%
Missing5
Missing (%)< 0.1%
Memory size14.7 MiB
2025-01-07T10:45:58.152888image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length11
Mean length11.0374042
Min length6

Characters and Unicode

Total characters21262323
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1024476 ?
Unique (%)53.2%

Sample

1st rowUSNM 1119015
2nd rowUSNM 55168
3rd rowUSNM 52536
4th rowUSNM E40844
5th rowUSNM 241160
ValueCountFrequency (%)
usnm 1926388
50.0%
31
 
< 0.1%
284908 16
 
< 0.1%
653324 13
 
< 0.1%
5357 11
 
< 0.1%
224878 10
 
< 0.1%
15490 10
 
< 0.1%
859036 10
 
< 0.1%
22869 10
 
< 0.1%
281850 9
 
< 0.1%
Other values (1352149) 1926301
50.0%
2025-01-07T10:45:58.948267image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 1928507
 
9.1%
U 1926495
 
9.1%
1926421
 
9.1%
N 1926388
 
9.1%
S 1926388
 
9.1%
1 1809864
 
8.5%
2 1247566
 
5.9%
3 1147864
 
5.4%
4 1110834
 
5.2%
5 1088355
 
5.1%
Other values (53) 5223641
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21262323
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 1928507
 
9.1%
U 1926495
 
9.1%
1926421
 
9.1%
N 1926388
 
9.1%
S 1926388
 
9.1%
1 1809864
 
8.5%
2 1247566
 
5.9%
3 1147864
 
5.4%
4 1110834
 
5.2%
5 1088355
 
5.1%
Other values (53) 5223641
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21262323
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 1928507
 
9.1%
U 1926495
 
9.1%
1926421
 
9.1%
N 1926388
 
9.1%
S 1926388
 
9.1%
1 1809864
 
8.5%
2 1247566
 
5.9%
3 1147864
 
5.4%
4 1110834
 
5.2%
5 1088355
 
5.1%
Other values (53) 5223641
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21262323
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 1928507
 
9.1%
U 1926495
 
9.1%
1926421
 
9.1%
N 1926388
 
9.1%
S 1926388
 
9.1%
1 1809864
 
8.5%
2 1247566
 
5.9%
3 1147864
 
5.4%
4 1110834
 
5.2%
5 1088355
 
5.1%
Other values (53) 5223641
24.6%

recordNumber
Text

Missing 

Distinct119495
Distinct (%)98.1%
Missing1804640
Missing (%)93.7%
Memory size14.7 MiB
2025-01-07T10:45:59.175364image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length87
Median length14
Mean length13.17353166
Min length1

Characters and Unicode

Total characters1603917
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique118866 ?
Unique (%)97.6%

Sample

1st rowUSNPC # 001298
2nd rowFPlrv_430
3rd rowH-2284
4th rowUSNPC # 066527
5th rowUSNPC # 009815
ValueCountFrequency (%)
88145
28.7%
usnpc 88064
28.6%
ullz 5209
 
1.7%
rh 1566
 
0.5%
k-rh 1555
 
0.5%
ce16007-event 223
 
0.1%
2208 102
 
< 0.1%
1430 92
 
< 0.1%
1513 80
 
< 0.1%
beauty 75
 
< 0.1%
Other values (119414) 122317
39.8%
2025-01-07T10:45:59.469637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
185675
 
11.6%
0 161175
 
10.0%
C 97557
 
6.1%
S 95231
 
5.9%
U 94869
 
5.9%
P 94146
 
5.9%
N 93453
 
5.8%
# 88221
 
5.5%
1 83004
 
5.2%
2 65151
 
4.1%
Other values (71) 545435
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1603917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
185675
 
11.6%
0 161175
 
10.0%
C 97557
 
6.1%
S 95231
 
5.9%
U 94869
 
5.9%
P 94146
 
5.9%
N 93453
 
5.8%
# 88221
 
5.5%
1 83004
 
5.2%
2 65151
 
4.1%
Other values (71) 545435
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1603917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
185675
 
11.6%
0 161175
 
10.0%
C 97557
 
6.1%
S 95231
 
5.9%
U 94869
 
5.9%
P 94146
 
5.9%
N 93453
 
5.8%
# 88221
 
5.5%
1 83004
 
5.2%
2 65151
 
4.1%
Other values (71) 545435
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1603917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
185675
 
11.6%
0 161175
 
10.0%
C 97557
 
6.1%
S 95231
 
5.9%
U 94869
 
5.9%
P 94146
 
5.9%
N 93453
 
5.8%
# 88221
 
5.5%
1 83004
 
5.2%
2 65151
 
4.1%
Other values (71) 545435
34.0%

recordedBy
Text

Missing 

Distinct37540
Distinct (%)3.2%
Missing764111
Missing (%)39.7%
Memory size14.7 MiB
2025-01-07T10:45:59.680404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24975
Median length156
Mean length23.05844881
Min length1

Characters and Unicode

Total characters26800420
Distinct characters99
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16583 ?
Unique (%)1.4%

Sample

1st rowVIMS for BLM/ MMS
2nd rowLgl Ecological Research Associates/ Environmental Science And Engineering For BLM/ MMS
3rd rowUniversity of Southern California
4th rowUnited States Fish Commission
5th rowUnited States Fish Commission
ValueCountFrequency (%)
mms 181011
 
4.2%
blm 181009
 
4.2%
for 178053
 
4.2%
fish 168374
 
3.9%
united 164153
 
3.8%
states 163489
 
3.8%
commission 157086
 
3.7%
149581
 
3.5%
of 101785
 
2.4%
j 101464
 
2.4%
Other values (19944) 2737862
63.9%
2025-01-07T10:45:59.965656image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3119233
 
11.6%
e 2082533
 
7.8%
i 1879315
 
7.0%
n 1616256
 
6.0%
t 1592703
 
5.9%
o 1549732
 
5.8%
s 1530048
 
5.7%
a 1499473
 
5.6%
r 1221276
 
4.6%
M 808831
 
3.0%
Other values (89) 9901020
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26800420
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3119233
 
11.6%
e 2082533
 
7.8%
i 1879315
 
7.0%
n 1616256
 
6.0%
t 1592703
 
5.9%
o 1549732
 
5.8%
s 1530048
 
5.7%
a 1499473
 
5.6%
r 1221276
 
4.6%
M 808831
 
3.0%
Other values (89) 9901020
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26800420
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3119233
 
11.6%
e 2082533
 
7.8%
i 1879315
 
7.0%
n 1616256
 
6.0%
t 1592703
 
5.9%
o 1549732
 
5.8%
s 1530048
 
5.7%
a 1499473
 
5.6%
r 1221276
 
4.6%
M 808831
 
3.0%
Other values (89) 9901020
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26800420
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3119233
 
11.6%
e 2082533
 
7.8%
i 1879315
 
7.0%
n 1616256
 
6.0%
t 1592703
 
5.9%
o 1549732
 
5.8%
s 1530048
 
5.7%
a 1499473
 
5.6%
r 1221276
 
4.6%
M 808831
 
3.0%
Other values (89) 9901020
36.9%

recordedByID
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

individualCount
Real number (ℝ)

Skewed 

Distinct1067
Distinct (%)0.1%
Missing156
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean6.257475067
Minimum0
Maximum19634
Zeros7832
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:00.044499image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q34
95-th percentile16
Maximum19634
Range19634
Interquartile range (IQR)3

Descriptive statistics

Standard deviation57.22157009
Coefficient of variation (CV)9.144514277
Kurtosis18624.73818
Mean6.257475067
Median Absolute Deviation (MAD)0
Skewness100.3828675
Sum12053380
Variance3274.308083
MonotonicityNot monotonic
2025-01-07T10:46:00.109062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 995782
51.7%
2 289569
 
15.0%
3 135771
 
7.0%
4 99105
 
5.1%
5 73928
 
3.8%
6 51745
 
2.7%
10 38953
 
2.0%
7 31375
 
1.6%
8 30170
 
1.6%
9 18501
 
1.0%
Other values (1057) 161338
 
8.4%
ValueCountFrequency (%)
0 7832
 
0.4%
1 995782
51.7%
2 289569
 
15.0%
3 135771
 
7.0%
4 99105
 
5.1%
ValueCountFrequency (%)
19634 1
< 0.1%
15284 1
< 0.1%
12500 1
< 0.1%
11404 1
< 0.1%
10000 2
< 0.1%

organismQuantity
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

organismQuantityType
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

sex
Text

Missing 

Distinct3
Distinct (%)< 0.1%
Missing1802980
Missing (%)93.6%
Memory size14.7 MiB
2025-01-07T10:46:00.152063image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length6
Mean length5.129864763
Min length4

Characters and Unicode

Total characters633092
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFEMALE
2nd rowFEMALE
3rd rowMALE
4th rowMALE
5th rowFEMALE
ValueCountFrequency (%)
female 68541
55.5%
male 54610
44.2%
hermaphrodite 262
 
0.2%
2025-01-07T10:46:00.252754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 192216
30.4%
M 123413
19.5%
A 123413
19.5%
L 123151
19.5%
F 68541
 
10.8%
H 524
 
0.1%
R 524
 
0.1%
P 262
 
< 0.1%
O 262
 
< 0.1%
D 262
 
< 0.1%
Other values (2) 524
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 633092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 192216
30.4%
M 123413
19.5%
A 123413
19.5%
L 123151
19.5%
F 68541
 
10.8%
H 524
 
0.1%
R 524
 
0.1%
P 262
 
< 0.1%
O 262
 
< 0.1%
D 262
 
< 0.1%
Other values (2) 524
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 633092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 192216
30.4%
M 123413
19.5%
A 123413
19.5%
L 123151
19.5%
F 68541
 
10.8%
H 524
 
0.1%
R 524
 
0.1%
P 262
 
< 0.1%
O 262
 
< 0.1%
D 262
 
< 0.1%
Other values (2) 524
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 633092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 192216
30.4%
M 123413
19.5%
A 123413
19.5%
L 123151
19.5%
F 68541
 
10.8%
H 524
 
0.1%
R 524
 
0.1%
P 262
 
< 0.1%
O 262
 
< 0.1%
D 262
 
< 0.1%
Other values (2) 524
 
0.1%

lifeStage
Text

Missing 

Distinct19
Distinct (%)0.1%
Missing1888856
Missing (%)98.1%
Memory size14.7 MiB
2025-01-07T10:46:00.306627image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.544262994
Min length3

Characters and Unicode

Total characters245652
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowLarva
2nd rowJuvenile
3rd rowLarva
4th rowJuvenile
5th rowLarva
ValueCountFrequency (%)
juvenile 18119
48.3%
adult 9874
26.3%
larva 7695
20.5%
immature 711
 
1.9%
mature 247
 
0.7%
subadult 244
 
0.7%
egg 142
 
0.4%
megalopa 131
 
0.3%
veliger 126
 
0.3%
zoea 95
 
0.3%
Other values (9) 153
 
0.4%
2025-01-07T10:46:00.416385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 37685
15.3%
u 29584
12.0%
l 28565
11.6%
v 25814
10.5%
i 18319
7.5%
n 18135
7.4%
J 18119
7.4%
a 17028
6.9%
t 11097
 
4.5%
d 10135
 
4.1%
Other values (25) 31171
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 245652
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 37685
15.3%
u 29584
12.0%
l 28565
11.6%
v 25814
10.5%
i 18319
7.5%
n 18135
7.4%
J 18119
7.4%
a 17028
6.9%
t 11097
 
4.5%
d 10135
 
4.1%
Other values (25) 31171
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 245652
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 37685
15.3%
u 29584
12.0%
l 28565
11.6%
v 25814
10.5%
i 18319
7.5%
n 18135
7.4%
J 18119
7.4%
a 17028
6.9%
t 11097
 
4.5%
d 10135
 
4.1%
Other values (25) 31171
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 245652
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 37685
15.3%
u 29584
12.0%
l 28565
11.6%
v 25814
10.5%
i 18319
7.5%
n 18135
7.4%
J 18119
7.4%
a 17028
6.9%
t 11097
 
4.5%
d 10135
 
4.1%
Other values (25) 31171
12.7%

reproductiveCondition
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

caste
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

behavior
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

vitality
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

establishmentMeans
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

degreeOfEstablishment
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

pathway
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

georeferenceVerificationStatus
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:00.462385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length7
Mean length6.997880495
Min length6

Characters and Unicode

Total characters13480668
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowPRESENT
2nd rowPRESENT
3rd rowPRESENT
4th rowPRESENT
5th rowPRESENT
ValueCountFrequency (%)
present 1922302
99.8%
absent 4089
 
0.2%
1993-09-09 1
 
< 0.1%
1938-09-22 1
 
< 0.1%
2025-01-07T10:46:00.570143image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 3848693
28.5%
N 1926391
14.3%
S 1926391
14.3%
T 1926391
14.3%
P 1922302
14.3%
R 1922302
14.3%
A 4089
 
< 0.1%
B 4089
 
< 0.1%
9 6
 
< 0.1%
- 4
 
< 0.1%
Other values (5) 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13480668
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 3848693
28.5%
N 1926391
14.3%
S 1926391
14.3%
T 1926391
14.3%
P 1922302
14.3%
R 1922302
14.3%
A 4089
 
< 0.1%
B 4089
 
< 0.1%
9 6
 
< 0.1%
- 4
 
< 0.1%
Other values (5) 10
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13480668
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 3848693
28.5%
N 1926391
14.3%
S 1926391
14.3%
T 1926391
14.3%
P 1922302
14.3%
R 1922302
14.3%
A 4089
 
< 0.1%
B 4089
 
< 0.1%
9 6
 
< 0.1%
- 4
 
< 0.1%
Other values (5) 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13480668
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 3848693
28.5%
N 1926391
14.3%
S 1926391
14.3%
T 1926391
14.3%
P 1922302
14.3%
R 1922302
14.3%
A 4089
 
< 0.1%
B 4089
 
< 0.1%
9 6
 
< 0.1%
- 4
 
< 0.1%
Other values (5) 10
 
< 0.1%
Distinct527
Distinct (%)< 0.1%
Missing1860
Missing (%)0.1%
Memory size14.7 MiB
2025-01-07T10:46:00.637323image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length167
Median length157
Mean length10.12228005
Min length3

Characters and Unicode

Total characters19480662
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique212 ?
Unique (%)< 0.1%

Sample

1st rowAlcohol (Ethanol)
2nd rowDry
3rd rowAlcohol (Ethanol)
4th rowDry
5th rowDry
ValueCountFrequency (%)
ethanol 907118
30.8%
dry 902342
30.6%
alcohol 897625
30.5%
slide 129646
 
4.4%
19548
 
0.7%
95 16839
 
0.6%
formalin 12585
 
0.4%
biorepository 12373
 
0.4%
isopropyl 10055
 
0.3%
sorting 6036
 
0.2%
Other values (40) 31872
 
1.1%
2025-01-07T10:46:00.782600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 2866431
14.7%
o 2797187
14.4%
h 1806308
 
9.3%
1021506
 
5.2%
r 954329
 
4.9%
t 939560
 
4.8%
n 936854
 
4.8%
a 925743
 
4.8%
y 923987
 
4.7%
E 913018
 
4.7%
Other values (43) 5395739
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19480662
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2866431
14.7%
o 2797187
14.4%
h 1806308
 
9.3%
1021506
 
5.2%
r 954329
 
4.9%
t 939560
 
4.8%
n 936854
 
4.8%
a 925743
 
4.8%
y 923987
 
4.7%
E 913018
 
4.7%
Other values (43) 5395739
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19480662
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2866431
14.7%
o 2797187
14.4%
h 1806308
 
9.3%
1021506
 
5.2%
r 954329
 
4.9%
t 939560
 
4.8%
n 936854
 
4.8%
a 925743
 
4.8%
y 923987
 
4.7%
E 913018
 
4.7%
Other values (43) 5395739
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19480662
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2866431
14.7%
o 2797187
14.4%
h 1806308
 
9.3%
1021506
 
5.2%
r 954329
 
4.9%
t 939560
 
4.8%
n 936854
 
4.8%
a 925743
 
4.8%
y 923987
 
4.7%
E 913018
 
4.7%
Other values (43) 5395739
27.7%

disposition
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean258.5
Minimum252
Maximum265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:00.839110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum252
5-th percentile252.65
Q1255.25
median258.5
Q3261.75
95-th percentile264.35
Maximum265
Range13
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation9.192388155
Coefficient of variation (CV)0.03556049577
Kurtosisnan
Mean258.5
Median Absolute Deviation (MAD)6.5
Skewnessnan
Sum517
Variance84.5
MonotonicityStrictly increasing
2025-01-07T10:46:00.887386image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
252 1
 
< 0.1%
265 1
 
< 0.1%
(Missing) 1926391
> 99.9%
ValueCountFrequency (%)
252 1
< 0.1%
265 1
< 0.1%
ValueCountFrequency (%)
265 1
< 0.1%
252 1
< 0.1%

associatedOccurrences
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean258.5
Minimum252
Maximum265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:00.932895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum252
5-th percentile252.65
Q1255.25
median258.5
Q3261.75
95-th percentile264.35
Maximum265
Range13
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation9.192388155
Coefficient of variation (CV)0.03556049577
Kurtosisnan
Mean258.5
Median Absolute Deviation (MAD)6.5
Skewnessnan
Sum517
Variance84.5
MonotonicityStrictly increasing
2025-01-07T10:46:00.979777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
252 1
 
< 0.1%
265 1
 
< 0.1%
(Missing) 1926391
> 99.9%
ValueCountFrequency (%)
252 1
< 0.1%
265 1
< 0.1%
ValueCountFrequency (%)
265 1
< 0.1%
252 1
< 0.1%

associatedReferences
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1965.5
Minimum1938
Maximum1993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:01.021293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1938
5-th percentile1940.75
Q11951.75
median1965.5
Q31979.25
95-th percentile1990.25
Maximum1993
Range55
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation38.89087297
Coefficient of variation (CV)0.01978675806
Kurtosisnan
Mean1965.5
Median Absolute Deviation (MAD)27.5
Skewnessnan
Sum3931
Variance1512.5
MonotonicityStrictly decreasing
2025-01-07T10:46:01.062292image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1993 1
 
< 0.1%
1938 1
 
< 0.1%
(Missing) 1926391
> 99.9%
ValueCountFrequency (%)
1938 1
< 0.1%
1993 1
< 0.1%
ValueCountFrequency (%)
1993 1
< 0.1%
1938 1
< 0.1%

associatedSequences
Text

Missing 

Distinct5098
Distinct (%)99.5%
Missing1921269
Missing (%)99.7%
Memory size14.7 MiB
2025-01-07T10:46:01.133806image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length1349
Median length49
Mean length85.4980484
Min length1

Characters and Unicode

Total characters438092
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5082 ?
Unique (%)99.2%

Sample

1st rowhttps://www.ncbi.nlm.nih.gov/gquery?term=AY426351;https://www.ncbi.nlm.nih.gov/gquery?term=AY379442;https://www.ncbi.nlm.nih.gov/gquery?term=AY426385
2nd rowhttps://www.ncbi.nlm.nih.gov/gquery?term=MH825989
3rd rowhttps://www.ncbi.nlm.nih.gov/gquery?term=MT223244
4th rowhttps://www.ncbi.nlm.nih.gov/gquery?term=MH826372
5th rowhttps://www.ncbi.nlm.nih.gov/gquery?term=KT792656
ValueCountFrequency (%)
https://www.ncbi.nlm.nih.gov/gquery?term=km521547 12
 
0.2%
https://www.ncbi.nlm.nih.gov/gquery?term=ku285912 2
 
< 0.1%
https://www.ncbi.nlm.nih.gov/gquery?term=kx832080 2
 
< 0.1%
https://www.ncbi.nlm.nih.gov/gquery?term=mh244118 2
 
< 0.1%
https://www.ncbi.nlm.nih.gov/gquery?term=srr9613700 2
 
< 0.1%
https://www.ncbi.nlm.nih.gov/gquery?term=kp739770 2
 
< 0.1%
https://www.ncbi.nlm.nih.gov/gquery?term=jq307001 2
 
< 0.1%
9 2
 
< 0.1%
https://www.ncbi.nlm.nih.gov/gquery?term=mk246580;https://www.ncbi.nlm.nih.gov/gquery?term=mk246484 2
 
< 0.1%
https://www.ncbi.nlm.nih.gov/gquery?term=mk246581;https://www.ncbi.nlm.nih.gov/gquery?term=mk246487 2
 
< 0.1%
Other values (5088) 5094
99.4%
2025-01-07T10:46:01.375319image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 35419
 
8.1%
n 26562
 
6.1%
w 26562
 
6.1%
/ 26562
 
6.1%
t 26562
 
6.1%
i 17708
 
4.0%
h 17708
 
4.0%
g 17708
 
4.0%
m 17708
 
4.0%
r 17708
 
4.0%
Other values (51) 207885
47.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 438092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 35419
 
8.1%
n 26562
 
6.1%
w 26562
 
6.1%
/ 26562
 
6.1%
t 26562
 
6.1%
i 17708
 
4.0%
h 17708
 
4.0%
g 17708
 
4.0%
m 17708
 
4.0%
r 17708
 
4.0%
Other values (51) 207885
47.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 438092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 35419
 
8.1%
n 26562
 
6.1%
w 26562
 
6.1%
/ 26562
 
6.1%
t 26562
 
6.1%
i 17708
 
4.0%
h 17708
 
4.0%
g 17708
 
4.0%
m 17708
 
4.0%
r 17708
 
4.0%
Other values (51) 207885
47.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 438092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 35419
 
8.1%
n 26562
 
6.1%
w 26562
 
6.1%
/ 26562
 
6.1%
t 26562
 
6.1%
i 17708
 
4.0%
h 17708
 
4.0%
g 17708
 
4.0%
m 17708
 
4.0%
r 17708
 
4.0%
Other values (51) 207885
47.5%

associatedTaxa
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean15.5
Minimum9
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:01.434205image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile9.65
Q112.25
median15.5
Q318.75
95-th percentile21.35
Maximum22
Range13
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation9.192388155
Coefficient of variation (CV)0.5930573004
Kurtosisnan
Mean15.5
Median Absolute Deviation (MAD)6.5
Skewnessnan
Sum31
Variance84.5
MonotonicityStrictly increasing
2025-01-07T10:46:01.480205image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
9 1
 
< 0.1%
22 1
 
< 0.1%
(Missing) 1926391
> 99.9%
ValueCountFrequency (%)
9 1
< 0.1%
22 1
< 0.1%
ValueCountFrequency (%)
22 1
< 0.1%
9 1
< 0.1%

otherCatalogNumbers
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

occurrenceRemarks
Text

Missing 

Distinct384906
Distinct (%)49.2%
Missing1144485
Missing (%)59.4%
Memory size14.7 MiB
2025-01-07T10:46:01.775321image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length48983
Median length1371
Mean length61.51201036
Min length1

Characters and Unicode

Total characters48096733
Distinct characters133
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique322690 ?
Unique (%)41.3%

Sample

1st rowJewett.; Stearns.
2nd rowBartsch
3rd row15 Nov. 1973; Jones, Dawson, del Rosario; Fitzgerald; NMNH-STRI Survey
4th rowU. S. B. Fish
5th rowC.R. Laws
ValueCountFrequency (%)
coll 143199
 
2.1%
of 115369
 
1.7%
and 111363
 
1.7%
a 107288
 
1.6%
by 89612
 
1.3%
87811
 
1.3%
2 65618
 
1.0%
3 63129
 
0.9%
was 62154
 
0.9%
formalin 58892
 
0.9%
Other values (238105) 5777747
86.5%
2025-01-07T10:46:02.149879image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5892887
 
12.3%
e 2965997
 
6.2%
o 2602001
 
5.4%
a 2414749
 
5.0%
i 2010061
 
4.2%
t 1978195
 
4.1%
n 1975689
 
4.1%
r 1877425
 
3.9%
s 1858443
 
3.9%
l 1812957
 
3.8%
Other values (123) 22708329
47.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48096733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5892887
 
12.3%
e 2965997
 
6.2%
o 2602001
 
5.4%
a 2414749
 
5.0%
i 2010061
 
4.2%
t 1978195
 
4.1%
n 1975689
 
4.1%
r 1877425
 
3.9%
s 1858443
 
3.9%
l 1812957
 
3.8%
Other values (123) 22708329
47.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48096733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5892887
 
12.3%
e 2965997
 
6.2%
o 2602001
 
5.4%
a 2414749
 
5.0%
i 2010061
 
4.2%
t 1978195
 
4.1%
n 1975689
 
4.1%
r 1877425
 
3.9%
s 1858443
 
3.9%
l 1812957
 
3.8%
Other values (123) 22708329
47.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48096733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5892887
 
12.3%
e 2965997
 
6.2%
o 2602001
 
5.4%
a 2414749
 
5.0%
i 2010061
 
4.2%
t 1978195
 
4.1%
n 1975689
 
4.1%
r 1877425
 
3.9%
s 1858443
 
3.9%
l 1812957
 
3.8%
Other values (123) 22708329
47.2%

organismID
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

organismName
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

organismScope
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

associatedOrganisms
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

previousIdentifications
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

organismRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

materialEntityID
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

materialEntityRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

verbatimLabel
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:02.212769image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length62
Median length48.5
Mean length48.5
Min length35

Characters and Unicode

Total characters97
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowNorth America, North Pacific Ocean, Gulf Of California, Mexico
2nd rowNorth America, United States, Texas
ValueCountFrequency (%)
north 3
21.4%
america 2
14.3%
pacific 1
 
7.1%
ocean 1
 
7.1%
gulf 1
 
7.1%
of 1
 
7.1%
california 1
 
7.1%
mexico 1
 
7.1%
united 1
 
7.1%
states 1
 
7.1%
2025-01-07T10:46:02.320334image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12
 
12.4%
i 8
 
8.2%
a 8
 
8.2%
e 7
 
7.2%
c 6
 
6.2%
t 6
 
6.2%
r 6
 
6.2%
o 5
 
5.2%
, 5
 
5.2%
f 4
 
4.1%
Other values (18) 30
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12
 
12.4%
i 8
 
8.2%
a 8
 
8.2%
e 7
 
7.2%
c 6
 
6.2%
t 6
 
6.2%
r 6
 
6.2%
o 5
 
5.2%
, 5
 
5.2%
f 4
 
4.1%
Other values (18) 30
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12
 
12.4%
i 8
 
8.2%
a 8
 
8.2%
e 7
 
7.2%
c 6
 
6.2%
t 6
 
6.2%
r 6
 
6.2%
o 5
 
5.2%
, 5
 
5.2%
f 4
 
4.1%
Other values (18) 30
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12
 
12.4%
i 8
 
8.2%
a 8
 
8.2%
e 7
 
7.2%
c 6
 
6.2%
t 6
 
6.2%
r 6
 
6.2%
o 5
 
5.2%
, 5
 
5.2%
f 4
 
4.1%
Other values (18) 30
30.9%

materialSampleID
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing1926391
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:02.368333image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters26
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTH_AMERICA
2nd rowNORTH_AMERICA
ValueCountFrequency (%)
north_america 2
100.0%
2025-01-07T10:46:02.464946image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 4
15.4%
A 4
15.4%
N 2
7.7%
O 2
7.7%
T 2
7.7%
H 2
7.7%
_ 2
7.7%
M 2
7.7%
E 2
7.7%
I 2
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 4
15.4%
A 4
15.4%
N 2
7.7%
O 2
7.7%
T 2
7.7%
H 2
7.7%
_ 2
7.7%
M 2
7.7%
E 2
7.7%
I 2
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 4
15.4%
A 4
15.4%
N 2
7.7%
O 2
7.7%
T 2
7.7%
H 2
7.7%
_ 2
7.7%
M 2
7.7%
E 2
7.7%
I 2
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 4
15.4%
A 4
15.4%
N 2
7.7%
O 2
7.7%
T 2
7.7%
H 2
7.7%
_ 2
7.7%
M 2
7.7%
E 2
7.7%
I 2
7.7%

eventID
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing1926392
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:02.516257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters39
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowNorth Pacific Ocean, Gulf Of California
ValueCountFrequency (%)
north 1
16.7%
pacific 1
16.7%
ocean 1
16.7%
gulf 1
16.7%
of 1
16.7%
california 1
16.7%
2025-01-07T10:46:02.617484image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5
12.8%
f 4
10.3%
i 4
10.3%
a 4
10.3%
c 3
 
7.7%
o 2
 
5.1%
r 2
 
5.1%
n 2
 
5.1%
l 2
 
5.1%
O 2
 
5.1%
Other values (9) 9
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5
12.8%
f 4
10.3%
i 4
10.3%
a 4
10.3%
c 3
 
7.7%
o 2
 
5.1%
r 2
 
5.1%
n 2
 
5.1%
l 2
 
5.1%
O 2
 
5.1%
Other values (9) 9
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5
12.8%
f 4
10.3%
i 4
10.3%
a 4
10.3%
c 3
 
7.7%
o 2
 
5.1%
r 2
 
5.1%
n 2
 
5.1%
l 2
 
5.1%
O 2
 
5.1%
Other values (9) 9
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5
12.8%
f 4
10.3%
i 4
10.3%
a 4
10.3%
c 3
 
7.7%
o 2
 
5.1%
r 2
 
5.1%
n 2
 
5.1%
l 2
 
5.1%
O 2
 
5.1%
Other values (9) 9
23.1%

parentEventID
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

eventType
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

fieldNumber
Text

Missing 

Distinct62652
Distinct (%)10.7%
Missing1339759
Missing (%)69.5%
Memory size14.7 MiB
2025-01-07T10:46:02.816862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length111
Median length63
Mean length13.61565474
Min length1

Characters and Unicode

Total characters7987406
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27490 ?
Unique (%)4.7%

Sample

1st rowMMS-CABP/02B-E4
2nd row4/III-23-TDS
3rd rowUSARP/EL/12/1002/USC
4th rowUSFC/A2059
5th rowUSFC/A5374
ValueCountFrequency (%)
mms-mafla/jar 17292
 
2.6%
bolland/rfb 7605
 
1.1%
humes 5243
 
0.8%
jpem 5029
 
0.8%
4975
 
0.8%
rh 2306
 
0.3%
k-rh 1557
 
0.2%
spm 1164
 
0.2%
mnhn-norfolk 1131
 
0.2%
haul 1040
 
0.2%
Other values (59086) 614438
92.8%
2025-01-07T10:46:03.107320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 742746
 
9.3%
S 650690
 
8.1%
M 501374
 
6.3%
- 480058
 
6.0%
A 421866
 
5.3%
1 403237
 
5.0%
0 377832
 
4.7%
C 368160
 
4.6%
2 360968
 
4.5%
U 266532
 
3.3%
Other values (72) 3413943
42.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7987406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 742746
 
9.3%
S 650690
 
8.1%
M 501374
 
6.3%
- 480058
 
6.0%
A 421866
 
5.3%
1 403237
 
5.0%
0 377832
 
4.7%
C 368160
 
4.6%
2 360968
 
4.5%
U 266532
 
3.3%
Other values (72) 3413943
42.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7987406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 742746
 
9.3%
S 650690
 
8.1%
M 501374
 
6.3%
- 480058
 
6.0%
A 421866
 
5.3%
1 403237
 
5.0%
0 377832
 
4.7%
C 368160
 
4.6%
2 360968
 
4.5%
U 266532
 
3.3%
Other values (72) 3413943
42.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7987406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 742746
 
9.3%
S 650690
 
8.1%
M 501374
 
6.3%
- 480058
 
6.0%
A 421866
 
5.3%
1 403237
 
5.0%
0 377832
 
4.7%
C 368160
 
4.6%
2 360968
 
4.5%
U 266532
 
3.3%
Other values (72) 3413943
42.7%

eventDate
Text

Missing 

Distinct45561
Distinct (%)3.7%
Missing688611
Missing (%)35.7%
Memory size14.7 MiB
2025-01-07T10:46:03.333026image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length10
Mean length9.825816662
Min length4

Characters and Unicode

Total characters12162219
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6824 ?
Unique (%)0.6%

Sample

1st row1976-03-03
2nd row1984-05-15
3rd row1964-03-15
4th row1883-08-31
5th row1909-03-02
ValueCountFrequency (%)
1915 6254
 
0.5%
1982-07-21 5684
 
0.5%
1981-07-06 5412
 
0.4%
1983-05-13 5155
 
0.4%
1982-11-19 5039
 
0.4%
1982-02-10 4461
 
0.4%
1981-11-09 4297
 
0.3%
1913 4293
 
0.3%
1982-05-10 4269
 
0.3%
1977-01-28/1977-02-13 3795
 
0.3%
Other values (45551) 1189123
96.1%
2025-01-07T10:46:03.619243image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2343420
19.3%
- 2329130
19.2%
0 1804499
14.8%
9 1499550
12.3%
2 828832
 
6.8%
8 778911
 
6.4%
7 716498
 
5.9%
6 564568
 
4.6%
5 436405
 
3.6%
3 431150
 
3.5%
Other values (7) 429256
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12162219
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2343420
19.3%
- 2329130
19.2%
0 1804499
14.8%
9 1499550
12.3%
2 828832
 
6.8%
8 778911
 
6.4%
7 716498
 
5.9%
6 564568
 
4.6%
5 436405
 
3.6%
3 431150
 
3.5%
Other values (7) 429256
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12162219
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2343420
19.3%
- 2329130
19.2%
0 1804499
14.8%
9 1499550
12.3%
2 828832
 
6.8%
8 778911
 
6.4%
7 716498
 
5.9%
6 564568
 
4.6%
5 436405
 
3.6%
3 431150
 
3.5%
Other values (7) 429256
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12162219
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2343420
19.3%
- 2329130
19.2%
0 1804499
14.8%
9 1499550
12.3%
2 828832
 
6.8%
8 778911
 
6.4%
7 716498
 
5.9%
6 564568
 
4.6%
5 436405
 
3.6%
3 431150
 
3.5%
Other values (7) 429256
 
3.5%

eventTime
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

startDayOfYear
Real number (ℝ)

Missing 

Distinct366
Distinct (%)< 0.1%
Missing842313
Missing (%)43.7%
Infinite0
Infinite (%)0.0%
Mean176.4091091
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:03.699636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile28
Q1100
median175
Q3248
95-th percentile332
Maximum366
Range365
Interquartile range (IQR)148

Descriptive statistics

Standard deviation95.36913082
Coefficient of variation (CV)0.5406134145
Kurtosis-1.020936972
Mean176.4091091
Median Absolute Deviation (MAD)74
Skewness0.05928782872
Sum191241587
Variance9095.271114
MonotonicityNot monotonic
2025-01-07T10:46:03.765139image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202 9215
 
0.5%
133 9048
 
0.5%
187 8343
 
0.4%
130 7952
 
0.4%
323 7925
 
0.4%
41 7863
 
0.4%
145 7055
 
0.4%
313 6543
 
0.3%
175 6524
 
0.3%
263 6356
 
0.3%
Other values (356) 1007256
52.3%
(Missing) 842313
43.7%
ValueCountFrequency (%)
1 1012
0.1%
2 2206
0.1%
3 1327
0.1%
4 1069
0.1%
5 1588
0.1%
ValueCountFrequency (%)
366 194
 
< 0.1%
365 1197
0.1%
364 864
< 0.1%
363 768
< 0.1%
362 923
< 0.1%

endDayOfYear
Unsupported

Missing  Rejected  Unsupported 

Missing842311
Missing (%)43.7%
Memory size14.7 MiB

year
Real number (ℝ)

Missing 

Distinct207
Distinct (%)< 0.1%
Missing689273
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean1958.619931
Minimum1806
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:03.830935image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1806
5-th percentile1888
Q11938
median1970
Q31981
95-th percentile2002
Maximum2024
Range218
Interquartile range (IQR)43

Descriptive statistics

Standard deviation33.77200218
Coefficient of variation (CV)0.01724275427
Kurtosis-0.1590675237
Mean1958.619931
Median Absolute Deviation (MAD)13
Skewness-0.8552102402
Sum2423047889
Variance1140.548132
MonotonicityNot monotonic
2025-01-07T10:46:03.892482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1977 73835
 
3.8%
1981 43749
 
2.3%
1976 42199
 
2.2%
1984 38196
 
2.0%
1982 38145
 
2.0%
1908 35299
 
1.8%
1983 34031
 
1.8%
1985 30482
 
1.6%
1964 28236
 
1.5%
1975 25013
 
1.3%
Other values (197) 847935
44.0%
(Missing) 689273
35.8%
ValueCountFrequency (%)
1806 1
 
< 0.1%
1809 1
 
< 0.1%
1816 3
< 0.1%
1817 1
 
< 0.1%
1818 1
 
< 0.1%
ValueCountFrequency (%)
2024 28
 
< 0.1%
2023 1677
0.1%
2022 980
0.1%
2021 667
 
< 0.1%
2020 96
 
< 0.1%

month
Real number (ℝ)

Missing 

Distinct12
Distinct (%)< 0.1%
Missing800939
Missing (%)41.6%
Infinite0
Infinite (%)0.0%
Mean6.351987731
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:03.945991image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q39
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.13032601
Coefficient of variation (CV)0.4928104623
Kurtosis-1.000604209
Mean6.351987731
Median Absolute Deviation (MAD)2
Skewness0.05549727094
Sum7148870
Variance9.798940932
MonotonicityNot monotonic
2025-01-07T10:46:03.994447image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 129894
 
6.7%
5 124558
 
6.5%
7 123176
 
6.4%
6 104255
 
5.4%
4 99639
 
5.2%
11 96677
 
5.0%
2 95459
 
5.0%
3 89439
 
4.6%
9 80447
 
4.2%
10 66176
 
3.4%
Other values (2) 115734
 
6.0%
(Missing) 800939
41.6%
ValueCountFrequency (%)
1 63333
3.3%
2 95459
5.0%
3 89439
4.6%
4 99639
5.2%
5 124558
6.5%
ValueCountFrequency (%)
12 52401
2.7%
11 96677
5.0%
10 66176
3.4%
9 80447
4.2%
8 129894
6.7%

day
Real number (ℝ)

Missing 

Distinct31
Distinct (%)< 0.1%
Missing887053
Missing (%)46.0%
Infinite0
Infinite (%)0.0%
Mean15.32565089
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:04.045593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q322
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.541752496
Coefficient of variation (CV)0.5573500633
Kurtosis-1.115069756
Mean15.32565089
Median Absolute Deviation (MAD)7
Skewness0.07233590415
Sum15928562
Variance72.96153569
MonotonicityNot monotonic
2025-01-07T10:46:04.101595image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
13 42864
 
2.2%
10 42434
 
2.2%
19 40651
 
2.1%
6 39463
 
2.0%
21 37986
 
2.0%
9 37781
 
2.0%
15 37214
 
1.9%
18 36290
 
1.9%
14 35493
 
1.8%
16 35080
 
1.8%
Other values (21) 654084
34.0%
(Missing) 887053
46.0%
ValueCountFrequency (%)
1 31620
1.6%
2 33326
1.7%
3 31847
1.7%
4 34956
1.8%
5 35035
1.8%
ValueCountFrequency (%)
31 17881
0.9%
30 27743
1.4%
29 26961
1.4%
28 29731
1.5%
27 28668
1.5%

verbatimEventDate
Text

Missing 

Distinct47776
Distinct (%)6.3%
Missing1173199
Missing (%)60.9%
Memory size14.7 MiB
2025-01-07T10:46:04.305148image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length181
Median length11
Mean length11.01797943
Min length1

Characters and Unicode

Total characters8298676
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15837 ?
Unique (%)2.1%

Sample

1st row-- --- ----
2nd row15 MAY 1984
3rd row15 MAR 1964
4th row03 MAR 1967
5th row31 AUG 1958
ValueCountFrequency (%)
275912
 
12.6%
may 68627
 
3.1%
aug 65853
 
3.0%
jul 61532
 
2.8%
apr 57935
 
2.6%
feb 53288
 
2.4%
jun 52783
 
2.4%
nov 52211
 
2.4%
mar 46122
 
2.1%
1977 42132
 
1.9%
Other values (8403) 1419007
64.6%
2025-01-07T10:46:04.592886image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1442208
17.4%
1 1077550
13.0%
9 807908
 
9.7%
- 749611
 
9.0%
2 340282
 
4.1%
7 334273
 
4.0%
0 322856
 
3.9%
8 301958
 
3.6%
6 296090
 
3.6%
A 274119
 
3.3%
Other values (71) 2351821
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8298676
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1442208
17.4%
1 1077550
13.0%
9 807908
 
9.7%
- 749611
 
9.0%
2 340282
 
4.1%
7 334273
 
4.0%
0 322856
 
3.9%
8 301958
 
3.6%
6 296090
 
3.6%
A 274119
 
3.3%
Other values (71) 2351821
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8298676
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1442208
17.4%
1 1077550
13.0%
9 807908
 
9.7%
- 749611
 
9.0%
2 340282
 
4.1%
7 334273
 
4.0%
0 322856
 
3.9%
8 301958
 
3.6%
6 296090
 
3.6%
A 274119
 
3.3%
Other values (71) 2351821
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8298676
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1442208
17.4%
1 1077550
13.0%
9 807908
 
9.7%
- 749611
 
9.0%
2 340282
 
4.1%
7 334273
 
4.0%
0 322856
 
3.9%
8 301958
 
3.6%
6 296090
 
3.6%
A 274119
 
3.3%
Other values (71) 2351821
28.3%

habitat
Text

Missing 

Distinct18961
Distinct (%)27.4%
Missing1857136
Missing (%)96.4%
Memory size14.7 MiB
2025-01-07T10:46:04.804789image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length235
Median length159
Mean length19.79818646
Min length1

Characters and Unicode

Total characters1371163
Distinct characters89
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13600 ?
Unique (%)19.6%

Sample

1st rowBeach with fresh water creek running into it
2nd rowFreshwater
3rd rowIn sand
4th rowMangrove
5th rowUnder rocks
ValueCountFrequency (%)
freshwater 9208
 
4.1%
in 6886
 
3.1%
on 6374
 
2.8%
reef 6192
 
2.8%
sand 6092
 
2.7%
coral 5812
 
2.6%
of 4886
 
2.2%
rocks 4639
 
2.1%
sp 4290
 
1.9%
intertidal 4238
 
1.9%
Other values (6965) 165798
73.9%
2025-01-07T10:46:05.078398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
155158
 
11.3%
e 134098
 
9.8%
a 117967
 
8.6%
r 101199
 
7.4%
n 83052
 
6.1%
s 82888
 
6.0%
o 79802
 
5.8%
t 71848
 
5.2%
i 60753
 
4.4%
l 60225
 
4.4%
Other values (79) 424173
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1371163
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
155158
 
11.3%
e 134098
 
9.8%
a 117967
 
8.6%
r 101199
 
7.4%
n 83052
 
6.1%
s 82888
 
6.0%
o 79802
 
5.8%
t 71848
 
5.2%
i 60753
 
4.4%
l 60225
 
4.4%
Other values (79) 424173
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1371163
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
155158
 
11.3%
e 134098
 
9.8%
a 117967
 
8.6%
r 101199
 
7.4%
n 83052
 
6.1%
s 82888
 
6.0%
o 79802
 
5.8%
t 71848
 
5.2%
i 60753
 
4.4%
l 60225
 
4.4%
Other values (79) 424173
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1371163
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
155158
 
11.3%
e 134098
 
9.8%
a 117967
 
8.6%
r 101199
 
7.4%
n 83052
 
6.1%
s 82888
 
6.0%
o 79802
 
5.8%
t 71848
 
5.2%
i 60753
 
4.4%
l 60225
 
4.4%
Other values (79) 424173
30.9%

samplingProtocol
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

sampleSizeValue
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

sampleSizeUnit
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

samplingEffort
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing1926392
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean24.1667
Minimum24.1667
Maximum24.1667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:05.141019image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum24.1667
5-th percentile24.1667
Q124.1667
median24.1667
Q324.1667
95-th percentile24.1667
Maximum24.1667
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean24.1667
Median Absolute Deviation (MAD)0
Skewnessnan
Sum24.1667
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:46:05.183750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
24.1667 1
 
< 0.1%
(Missing) 1926392
> 99.9%
ValueCountFrequency (%)
24.1667 1
< 0.1%
ValueCountFrequency (%)
24.1667 1
< 0.1%

fieldNotes
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing1926392
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean-110.283
Minimum-110.283
Maximum-110.283
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size14.7 MiB
2025-01-07T10:46:05.327627image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-110.283
5-th percentile-110.283
Q1-110.283
median-110.283
Q3-110.283
95-th percentile-110.283
Maximum-110.283
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean-110.283
Median Absolute Deviation (MAD)0
Skewnessnan
Sum-110.283
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:46:05.375627image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
-110.283 1
 
< 0.1%
(Missing) 1926392
> 99.9%
ValueCountFrequency (%)
-110.283 1
< 0.1%
ValueCountFrequency (%)
-110.283 1
< 0.1%

eventRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

locationID
Text

Missing 

Distinct94703
Distinct (%)10.0%
Missing984066
Missing (%)51.1%
Memory size14.7 MiB
2025-01-07T10:46:05.592779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length37768
Median length134
Mean length4.4719158
Min length1

Characters and Unicode

Total characters4214007
Distinct characters95
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52904 ?
Unique (%)5.6%

Sample

1st rowE4
2nd rowNR 12-4 ID 101
3rd row23
4th row1002
5th row2059
ValueCountFrequency (%)
not 12392
 
1.2%
rec 12070
 
1.2%
4 8476
 
0.8%
rhb 7696
 
0.7%
rfb 7623
 
0.7%
1 7614
 
0.7%
2 6232
 
0.6%
3 5496
 
0.5%
gs 5168
 
0.5%
6 5011
 
0.5%
Other values (80921) 965661
92.5%
2025-01-07T10:46:05.901095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 474584
 
11.3%
2 394541
 
9.4%
0 331952
 
7.9%
5 296061
 
7.0%
3 287737
 
6.8%
4 264333
 
6.3%
- 262376
 
6.2%
6 216672
 
5.1%
7 190959
 
4.5%
8 180969
 
4.3%
Other values (85) 1313823
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4214007
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 474584
 
11.3%
2 394541
 
9.4%
0 331952
 
7.9%
5 296061
 
7.0%
3 287737
 
6.8%
4 264333
 
6.3%
- 262376
 
6.2%
6 216672
 
5.1%
7 190959
 
4.5%
8 180969
 
4.3%
Other values (85) 1313823
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4214007
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 474584
 
11.3%
2 394541
 
9.4%
0 331952
 
7.9%
5 296061
 
7.0%
3 287737
 
6.8%
4 264333
 
6.3%
- 262376
 
6.2%
6 216672
 
5.1%
7 190959
 
4.5%
8 180969
 
4.3%
Other values (85) 1313823
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4214007
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 474584
 
11.3%
2 394541
 
9.4%
0 331952
 
7.9%
5 296061
 
7.0%
3 287737
 
6.8%
4 264333
 
6.3%
- 262376
 
6.2%
6 216672
 
5.1%
7 190959
 
4.5%
8 180969
 
4.3%
Other values (85) 1313823
31.2%

higherGeographyID
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

higherGeography
Text

Missing 

Distinct12370
Distinct (%)0.7%
Missing67831
Missing (%)3.5%
Memory size14.7 MiB
2025-01-07T10:46:06.103566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length126
Median length104
Mean length36.17342494
Min length4

Characters and Unicode

Total characters67230553
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3190 ?
Unique (%)0.2%

Sample

1st rowNorth Atlantic Ocean, United States
2nd rowNorth Atlantic Ocean, Gulf of Mexico, United States, Florida
3rd rowNorth Atlantic Ocean, Caribbean Sea, Barbados
4th rowNorth Atlantic Ocean, Gulf of Mexico, United States, Florida
5th rowPhilippines
ValueCountFrequency (%)
ocean 1259909
 
13.4%
north 1098149
 
11.7%
united 886190
 
9.4%
states 871608
 
9.3%
atlantic 718309
 
7.7%
pacific 437003
 
4.7%
mexico 248368
 
2.6%
of 243369
 
2.6%
gulf 228771
 
2.4%
south 203325
 
2.2%
Other values (4652) 3191450
34.0%
2025-01-07T10:46:06.382797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7527889
 
11.2%
a 6865365
 
10.2%
t 6256807
 
9.3%
i 4780196
 
7.1%
e 4733947
 
7.0%
n 4584442
 
6.8%
c 3760391
 
5.6%
o 2897132
 
4.3%
, 2857287
 
4.2%
r 2272065
 
3.4%
Other values (67) 20695032
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67230553
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7527889
 
11.2%
a 6865365
 
10.2%
t 6256807
 
9.3%
i 4780196
 
7.1%
e 4733947
 
7.0%
n 4584442
 
6.8%
c 3760391
 
5.6%
o 2897132
 
4.3%
, 2857287
 
4.2%
r 2272065
 
3.4%
Other values (67) 20695032
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67230553
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7527889
 
11.2%
a 6865365
 
10.2%
t 6256807
 
9.3%
i 4780196
 
7.1%
e 4733947
 
7.0%
n 4584442
 
6.8%
c 3760391
 
5.6%
o 2897132
 
4.3%
, 2857287
 
4.2%
r 2272065
 
3.4%
Other values (67) 20695032
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67230553
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7527889
 
11.2%
a 6865365
 
10.2%
t 6256807
 
9.3%
i 4780196
 
7.1%
e 4733947
 
7.0%
n 4584442
 
6.8%
c 3760391
 
5.6%
o 2897132
 
4.3%
, 2857287
 
4.2%
r 2272065
 
3.4%
Other values (67) 20695032
30.8%

continent
Text

Missing 

Distinct7
Distinct (%)< 0.1%
Missing1027391
Missing (%)53.3%
Memory size14.7 MiB
2025-01-07T10:46:06.448047image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length9.980899931
Min length4

Characters and Unicode

Total characters8972849
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTH_AMERICA
2nd rowASIA
3rd rowNORTH_AMERICA
4th rowOCEANIA
5th rowNORTH_AMERICA
ValueCountFrequency (%)
north_america 475004
52.8%
oceania 155883
 
17.3%
asia 135716
 
15.1%
south_america 44254
 
4.9%
africa 39371
 
4.4%
europe 33879
 
3.8%
antarctica 14895
 
1.7%
2025-01-07T10:46:06.557845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1745141
19.4%
R 1082407
12.1%
I 865123
9.6%
C 744302
8.3%
E 742899
8.3%
O 709020
7.9%
N 645782
 
7.2%
T 549048
 
6.1%
H 519258
 
5.8%
_ 519258
 
5.8%
Other values (5) 850611
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8972849
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1745141
19.4%
R 1082407
12.1%
I 865123
9.6%
C 744302
8.3%
E 742899
8.3%
O 709020
7.9%
N 645782
 
7.2%
T 549048
 
6.1%
H 519258
 
5.8%
_ 519258
 
5.8%
Other values (5) 850611
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8972849
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1745141
19.4%
R 1082407
12.1%
I 865123
9.6%
C 744302
8.3%
E 742899
8.3%
O 709020
7.9%
N 645782
 
7.2%
T 549048
 
6.1%
H 519258
 
5.8%
_ 519258
 
5.8%
Other values (5) 850611
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8972849
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1745141
19.4%
R 1082407
12.1%
I 865123
9.6%
C 744302
8.3%
E 742899
8.3%
O 709020
7.9%
N 645782
 
7.2%
T 549048
 
6.1%
H 519258
 
5.8%
_ 519258
 
5.8%
Other values (5) 850611
9.5%

waterBody
Text

Missing 

Distinct1655
Distinct (%)0.1%
Missing666651
Missing (%)34.6%
Memory size14.7 MiB
2025-01-07T10:46:06.745098image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length76
Median length75
Mean length24.49184833
Min length7

Characters and Unicode

Total characters30853410
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique510 ?
Unique (%)< 0.1%

Sample

1st rowNorth Atlantic Ocean
2nd rowNorth Atlantic Ocean, Gulf of Mexico
3rd rowNorth Atlantic Ocean, Caribbean Sea
4th rowNorth Atlantic Ocean, Gulf of Mexico
5th rowAntarctic Ocean
ValueCountFrequency (%)
ocean 1259434
26.1%
north 998553
20.7%
atlantic 718247
14.9%
pacific 436962
 
9.1%
of 231313
 
4.8%
gulf 228638
 
4.7%
sea 193896
 
4.0%
mexico 187756
 
3.9%
south 160377
 
3.3%
caribbean 89358
 
1.9%
Other values (1319) 318010
 
6.6%
2025-01-07T10:46:07.005808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3562802
11.5%
c 3175906
10.3%
a 3113538
 
10.1%
t 2738941
 
8.9%
n 2331622
 
7.6%
i 2082746
 
6.8%
e 1823700
 
5.9%
o 1648330
 
5.3%
O 1261125
 
4.1%
r 1218140
 
3.9%
Other values (53) 7896560
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30853410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3562802
11.5%
c 3175906
10.3%
a 3113538
 
10.1%
t 2738941
 
8.9%
n 2331622
 
7.6%
i 2082746
 
6.8%
e 1823700
 
5.9%
o 1648330
 
5.3%
O 1261125
 
4.1%
r 1218140
 
3.9%
Other values (53) 7896560
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30853410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3562802
11.5%
c 3175906
10.3%
a 3113538
 
10.1%
t 2738941
 
8.9%
n 2331622
 
7.6%
i 2082746
 
6.8%
e 1823700
 
5.9%
o 1648330
 
5.3%
O 1261125
 
4.1%
r 1218140
 
3.9%
Other values (53) 7896560
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30853410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3562802
11.5%
c 3175906
10.3%
a 3113538
 
10.1%
t 2738941
 
8.9%
n 2331622
 
7.6%
i 2082746
 
6.8%
e 1823700
 
5.9%
o 1648330
 
5.3%
O 1261125
 
4.1%
r 1218140
 
3.9%
Other values (53) 7896560
25.6%

islandGroup
Text

Missing 

Distinct20
Distinct (%)2.6%
Missing1925623
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:07.072313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length15
Mean length14.52857143
Min length5

Characters and Unicode

Total characters11187
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.8%

Sample

1st rowSociety Islands
2nd rowSociety Islands
3rd rowSociety Islands
4th rowSociety Islands
5th rowSociety Islands
ValueCountFrequency (%)
islands 707
47.0%
society 679
45.2%
exuma 20
 
1.3%
south 12
 
0.8%
sandwich 12
 
0.8%
florida 10
 
0.7%
keys 10
 
0.7%
pacific 10
 
0.7%
carolina 8
 
0.5%
aleutian 7
 
0.5%
Other values (14) 28
 
1.9%
2025-01-07T10:46:07.192725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 1446
12.9%
a 803
 
7.2%
l 751
 
6.7%
n 748
 
6.7%
i 743
 
6.6%
d 738
 
6.6%
733
 
6.6%
o 722
 
6.5%
c 713
 
6.4%
e 711
 
6.4%
Other values (25) 3079
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11187
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 1446
12.9%
a 803
 
7.2%
l 751
 
6.7%
n 748
 
6.7%
i 743
 
6.6%
d 738
 
6.6%
733
 
6.6%
o 722
 
6.5%
c 713
 
6.4%
e 711
 
6.4%
Other values (25) 3079
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11187
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 1446
12.9%
a 803
 
7.2%
l 751
 
6.7%
n 748
 
6.7%
i 743
 
6.6%
d 738
 
6.6%
733
 
6.6%
o 722
 
6.5%
c 713
 
6.4%
e 711
 
6.4%
Other values (25) 3079
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11187
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 1446
12.9%
a 803
 
7.2%
l 751
 
6.7%
n 748
 
6.7%
i 743
 
6.6%
d 738
 
6.6%
733
 
6.6%
o 722
 
6.5%
c 713
 
6.4%
e 711
 
6.4%
Other values (25) 3079
27.5%

island
Text

Missing 

Distinct58
Distinct (%)5.9%
Missing1925415
Missing (%)99.9%
Memory size14.7 MiB
2025-01-07T10:46:07.287630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length25
Median length6
Mean length6.676891616
Min length4

Characters and Unicode

Total characters6530
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)3.4%

Sample

1st rowMoorea
2nd rowMoorea
3rd rowShikoku
4th rowOahu
5th rowMoorea
ValueCountFrequency (%)
moorea 674
60.4%
oahu 147
 
13.2%
island 91
 
8.2%
great 20
 
1.8%
exuma 20
 
1.8%
eniwetok 13
 
1.2%
nunivak 13
 
1.2%
bonaire 11
 
1.0%
key 10
 
0.9%
west 10
 
0.9%
Other values (58) 106
 
9.5%
2025-01-07T10:46:07.440661image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1430
21.9%
a 1060
16.2%
e 771
11.8%
r 737
11.3%
M 683
10.5%
u 225
 
3.4%
n 186
 
2.8%
h 170
 
2.6%
O 154
 
2.4%
137
 
2.1%
Other values (39) 977
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6530
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1430
21.9%
a 1060
16.2%
e 771
11.8%
r 737
11.3%
M 683
10.5%
u 225
 
3.4%
n 186
 
2.8%
h 170
 
2.6%
O 154
 
2.4%
137
 
2.1%
Other values (39) 977
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6530
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1430
21.9%
a 1060
16.2%
e 771
11.8%
r 737
11.3%
M 683
10.5%
u 225
 
3.4%
n 186
 
2.8%
h 170
 
2.6%
O 154
 
2.4%
137
 
2.1%
Other values (39) 977
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6530
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1430
21.9%
a 1060
16.2%
e 771
11.8%
r 737
11.3%
M 683
10.5%
u 225
 
3.4%
n 186
 
2.8%
h 170
 
2.6%
O 154
 
2.4%
137
 
2.1%
Other values (39) 977
15.0%

countryCode
Text

Missing 

Distinct239
Distinct (%)< 0.1%
Missing110759
Missing (%)5.7%
Memory size14.7 MiB
2025-01-07T10:46:07.623471image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3631268
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowUS
2nd rowUS
3rd rowBB
4th rowUS
5th rowPH
ValueCountFrequency (%)
us 868583
47.8%
ph 93802
 
5.2%
mx 59371
 
3.3%
pa 46369
 
2.6%
aq 44802
 
2.5%
jp 38538
 
2.1%
cu 30147
 
1.7%
ca 28674
 
1.6%
jm 27586
 
1.5%
pf 27226
 
1.5%
Other values (229) 550536
30.3%
2025-01-07T10:46:07.861670image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 948161
26.1%
S 926976
25.5%
P 250779
 
6.9%
A 177982
 
4.9%
M 160911
 
4.4%
H 143259
 
3.9%
C 133182
 
3.7%
B 95322
 
2.6%
J 78390
 
2.2%
G 66596
 
1.8%
Other values (16) 649710
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3631268
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 948161
26.1%
S 926976
25.5%
P 250779
 
6.9%
A 177982
 
4.9%
M 160911
 
4.4%
H 143259
 
3.9%
C 133182
 
3.7%
B 95322
 
2.6%
J 78390
 
2.2%
G 66596
 
1.8%
Other values (16) 649710
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3631268
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 948161
26.1%
S 926976
25.5%
P 250779
 
6.9%
A 177982
 
4.9%
M 160911
 
4.4%
H 143259
 
3.9%
C 133182
 
3.7%
B 95322
 
2.6%
J 78390
 
2.2%
G 66596
 
1.8%
Other values (16) 649710
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3631268
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 948161
26.1%
S 926976
25.5%
P 250779
 
6.9%
A 177982
 
4.9%
M 160911
 
4.4%
H 143259
 
3.9%
C 133182
 
3.7%
B 95322
 
2.6%
J 78390
 
2.2%
G 66596
 
1.8%
Other values (16) 649710
17.9%

stateProvince
Text

Missing 

Distinct1326
Distinct (%)0.1%
Missing943673
Missing (%)49.0%
Memory size14.7 MiB
2025-01-07T10:46:08.060353image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length51
Median length39
Mean length9.182679705
Min length3

Characters and Unicode

Total characters9024003
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique281 ?
Unique (%)< 0.1%

Sample

1st rowFlorida
2nd rowFlorida
3rd rowMassachusetts
4th rowQuezon
5th rowNewfoundland
ValueCountFrequency (%)
florida 157981
 
13.1%
massachusetts 103383
 
8.6%
california 57085
 
4.7%
carolina 53929
 
4.5%
texas 43591
 
3.6%
alaska 41859
 
3.5%
north 31994
 
2.7%
louisiana 28645
 
2.4%
hawaii 26401
 
2.2%
south 26211
 
2.2%
Other values (1250) 635019
52.7%
2025-01-07T10:46:08.330950image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1427949
15.8%
i 809015
 
9.0%
s 773254
 
8.6%
o 650882
 
7.2%
r 519439
 
5.8%
l 506660
 
5.6%
n 498668
 
5.5%
e 457618
 
5.1%
t 400633
 
4.4%
u 277325
 
3.1%
Other values (60) 2702560
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9024003
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1427949
15.8%
i 809015
 
9.0%
s 773254
 
8.6%
o 650882
 
7.2%
r 519439
 
5.8%
l 506660
 
5.6%
n 498668
 
5.5%
e 457618
 
5.1%
t 400633
 
4.4%
u 277325
 
3.1%
Other values (60) 2702560
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9024003
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1427949
15.8%
i 809015
 
9.0%
s 773254
 
8.6%
o 650882
 
7.2%
r 519439
 
5.8%
l 506660
 
5.6%
n 498668
 
5.5%
e 457618
 
5.1%
t 400633
 
4.4%
u 277325
 
3.1%
Other values (60) 2702560
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9024003
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1427949
15.8%
i 809015
 
9.0%
s 773254
 
8.6%
o 650882
 
7.2%
r 519439
 
5.8%
l 506660
 
5.6%
n 498668
 
5.5%
e 457618
 
5.1%
t 400633
 
4.4%
u 277325
 
3.1%
Other values (60) 2702560
29.9%

county
Text

Missing 

Distinct2594
Distinct (%)1.9%
Missing1786420
Missing (%)92.7%
Memory size14.7 MiB
2025-01-07T10:46:08.533785image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length46
Median length43
Mean length14.35974795
Min length3

Characters and Unicode

Total characters2009977
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique558 ?
Unique (%)0.4%

Sample

1st rowCumberland County
2nd rowAllamakee County
3rd rowSt. Lucie County
4th rowDelaware County
5th rowKimble County
ValueCountFrequency (%)
county 135423
45.4%
st 3893
 
1.3%
parish 3203
 
1.1%
monroe 3117
 
1.0%
lucie 2649
 
0.9%
montgomery 2553
 
0.9%
san 2117
 
0.7%
prince 1875
 
0.6%
george's 1763
 
0.6%
jackson 1748
 
0.6%
Other values (2256) 139876
46.9%
2025-01-07T10:46:08.808726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 223770
11.1%
o 216846
10.8%
t 181049
 
9.0%
u 160924
 
8.0%
158244
 
7.9%
C 152414
 
7.6%
y 151819
 
7.6%
e 105735
 
5.3%
a 103265
 
5.1%
r 74023
 
3.7%
Other values (55) 481888
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2009977
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 223770
11.1%
o 216846
10.8%
t 181049
 
9.0%
u 160924
 
8.0%
158244
 
7.9%
C 152414
 
7.6%
y 151819
 
7.6%
e 105735
 
5.3%
a 103265
 
5.1%
r 74023
 
3.7%
Other values (55) 481888
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2009977
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 223770
11.1%
o 216846
10.8%
t 181049
 
9.0%
u 160924
 
8.0%
158244
 
7.9%
C 152414
 
7.6%
y 151819
 
7.6%
e 105735
 
5.3%
a 103265
 
5.1%
r 74023
 
3.7%
Other values (55) 481888
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2009977
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 223770
11.1%
o 216846
10.8%
t 181049
 
9.0%
u 160924
 
8.0%
158244
 
7.9%
C 152414
 
7.6%
y 151819
 
7.6%
e 105735
 
5.3%
a 103265
 
5.1%
r 74023
 
3.7%
Other values (55) 481888
24.0%

municipality
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

locality
Text

Missing 

Distinct204742
Distinct (%)15.9%
Missing642386
Missing (%)33.3%
Memory size14.7 MiB
2025-01-07T10:46:09.047767image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21793
Median length378
Mean length29.00482474
Min length1

Characters and Unicode

Total characters37242398
Distinct characters139
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126316 ?
Unique (%)9.8%

Sample

1st rowoff Delaware
2nd rowW Coast
3rd rowCape Sable, West Of
4th rowAntarctic Peninsula
5th rowGeorges Bank
ValueCountFrequency (%)
island 342357
 
5.6%
of 336472
 
5.5%
off 252665
 
4.1%
bay 137534
 
2.2%
islands 98147
 
1.6%
bank 84597
 
1.4%
south 74630
 
1.2%
georges 66663
 
1.1%
florida 63432
 
1.0%
river 63370
 
1.0%
Other values (77326) 4636608
75.3%
2025-01-07T10:46:09.341829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4869900
 
13.1%
a 3498938
 
9.4%
e 2451391
 
6.6%
o 2297059
 
6.2%
n 2155175
 
5.8%
r 1674733
 
4.5%
s 1629255
 
4.4%
i 1598121
 
4.3%
l 1584743
 
4.3%
t 1476204
 
4.0%
Other values (129) 14006879
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37242398
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4869900
 
13.1%
a 3498938
 
9.4%
e 2451391
 
6.6%
o 2297059
 
6.2%
n 2155175
 
5.8%
r 1674733
 
4.5%
s 1629255
 
4.4%
i 1598121
 
4.3%
l 1584743
 
4.3%
t 1476204
 
4.0%
Other values (129) 14006879
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37242398
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4869900
 
13.1%
a 3498938
 
9.4%
e 2451391
 
6.6%
o 2297059
 
6.2%
n 2155175
 
5.8%
r 1674733
 
4.5%
s 1629255
 
4.4%
i 1598121
 
4.3%
l 1584743
 
4.3%
t 1476204
 
4.0%
Other values (129) 14006879
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37242398
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4869900
 
13.1%
a 3498938
 
9.4%
e 2451391
 
6.6%
o 2297059
 
6.2%
n 2155175
 
5.8%
r 1674733
 
4.5%
s 1629255
 
4.4%
i 1598121
 
4.3%
l 1584743
 
4.3%
t 1476204
 
4.0%
Other values (129) 14006879
37.6%

verbatimLocality
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

verbatimElevation
Unsupported

Missing  Rejected  Unsupported 

Missing1925931
Missing (%)> 99.9%
Memory size14.7 MiB

verticalDatum
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

verbatimDepth
Text

Missing 

Distinct1530
Distinct (%)5.8%
Missing1900149
Missing (%)98.6%
Memory size14.7 MiB
2025-01-07T10:46:09.538492image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length99
Median length91
Mean length13.43716659
Min length1

Characters and Unicode

Total characters352645
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique721 ?
Unique (%)2.7%

Sample

1st rowSurface
2nd rowmax depth 1772 ft
3rd rowsurface
4th rowIntertidal
5th rowIntertidal
ValueCountFrequency (%)
intertidal 11932
23.4%
surface 4085
 
8.0%
recorded 2871
 
5.6%
depths 2850
 
5.6%
multiple 2846
 
5.6%
shore 1165
 
2.3%
0-300 1120
 
2.2%
0 1069
 
2.1%
depth 1023
 
2.0%
low 964
 
1.9%
Other values (1043) 21003
41.2%
2025-01-07T10:46:09.803374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 36687
 
10.4%
e 35142
 
10.0%
r 25391
 
7.2%
24684
 
7.0%
d 24177
 
6.9%
l 20651
 
5.9%
a 20481
 
5.8%
i 19392
 
5.5%
0 16029
 
4.5%
n 14727
 
4.2%
Other values (69) 115284
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 352645
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 36687
 
10.4%
e 35142
 
10.0%
r 25391
 
7.2%
24684
 
7.0%
d 24177
 
6.9%
l 20651
 
5.9%
a 20481
 
5.8%
i 19392
 
5.5%
0 16029
 
4.5%
n 14727
 
4.2%
Other values (69) 115284
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 352645
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 36687
 
10.4%
e 35142
 
10.0%
r 25391
 
7.2%
24684
 
7.0%
d 24177
 
6.9%
l 20651
 
5.9%
a 20481
 
5.8%
i 19392
 
5.5%
0 16029
 
4.5%
n 14727
 
4.2%
Other values (69) 115284
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 352645
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 36687
 
10.4%
e 35142
 
10.0%
r 25391
 
7.2%
24684
 
7.0%
d 24177
 
6.9%
l 20651
 
5.9%
a 20481
 
5.8%
i 19392
 
5.5%
0 16029
 
4.5%
n 14727
 
4.2%
Other values (69) 115284
32.7%

minimumDistanceAboveSurfaceInMeters
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

maximumDistanceAboveSurfaceInMeters
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

locationAccordingTo
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

locationRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

decimalLatitude
Real number (ℝ)

Missing 

Distinct70087
Distinct (%)7.0%
Missing927346
Missing (%)48.1%
Infinite0
Infinite (%)0.0%
Mean19.53293445
Minimum-90
Maximum86.85
Zeros126
Zeros (%)< 0.1%
Negative152585
Negative (%)7.9%
Memory size14.7 MiB
2025-01-07T10:46:09.875880image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-90
5-th percentile-56.108
Q111.5
median27.8867
Q338.2881
95-th percentile45.75397
Maximum86.85
Range176.85
Interquartile range (IQR)26.7881

Descriptive statistics

Standard deviation28.76997469
Coefficient of variation (CV)1.472895676
Kurtosis2.539179044
Mean19.53293445
Median Absolute Deviation (MAD)11.395
Skewness-1.700199564
Sum19514319.57
Variance827.7114436
MonotonicityNot monotonic
2025-01-07T10:46:09.937737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.58 10487
 
0.5%
40.6583 8821
 
0.5%
26.17 7319
 
0.4%
26.5 5153
 
0.3%
26.97 3942
 
0.2%
25.7883 3457
 
0.2%
9.4 3081
 
0.2%
40.895 2590
 
0.1%
40.66 2520
 
0.1%
25.2967 2475
 
0.1%
Other values (70077) 949202
49.3%
(Missing) 927346
48.1%
ValueCountFrequency (%)
-90 1
 
< 0.1%
-88.983 1
 
< 0.1%
-87.55 3
 
< 0.1%
-82.375 11
< 0.1%
-78.9167 3
 
< 0.1%
ValueCountFrequency (%)
86.85 1
 
< 0.1%
86.618 1
 
< 0.1%
85.9733 4
< 0.1%
85.9583 5
< 0.1%
85.6183 1
 
< 0.1%

decimalLongitude
Real number (ℝ)

Missing 

Distinct74625
Distinct (%)7.5%
Missing927346
Missing (%)48.1%
Infinite0
Infinite (%)0.0%
Mean-49.51288993
Minimum-180
Maximum180
Zeros13
Zeros (%)< 0.1%
Negative826266
Negative (%)42.9%
Memory size14.7 MiB
2025-01-07T10:46:10.100688image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-180
5-th percentile-132.4
Q1-86.0583
median-77.2831
Q3-64.1125
95-th percentile134.07
Maximum180
Range360
Interquartile range (IQR)21.9458

Descriptive statistics

Standard deviation81.34966635
Coefficient of variation (CV)-1.642999762
Kurtosis1.251841924
Mean-49.51288993
Median Absolute Deviation (MAD)10.5081
Skewness1.527721701
Sum-49465704.14
Variance6617.768215
MonotonicityNot monotonic
2025-01-07T10:46:10.159195image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-80.1 10464
 
0.5%
127.848 4532
 
0.2%
-67.7683 4213
 
0.2%
-80.13 3736
 
0.2%
-82.7 3516
 
0.2%
-67.77 2821
 
0.1%
-66.775 2592
 
0.1%
-81.6633 2462
 
0.1%
-70.6731 2397
 
0.1%
-67.755 2356
 
0.1%
Other values (74615) 959958
49.8%
(Missing) 927346
48.1%
ValueCountFrequency (%)
-180 8
 
< 0.1%
-179.994 2
 
< 0.1%
-179.98 11
< 0.1%
-179.971 1
 
< 0.1%
-179.97 26
< 0.1%
ValueCountFrequency (%)
180 27
< 0.1%
179.994 1
 
< 0.1%
179.98 16
< 0.1%
179.977 1
 
< 0.1%
179.954 1
 
< 0.1%

coordinateUncertaintyInMeters
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

coordinatePrecision
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

pointRadiusSpatialFit
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB
Distinct9
Distinct (%)< 0.1%
Missing1246885
Missing (%)64.7%
Memory size14.7 MiB
2025-01-07T10:46:10.209521image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length23
Mean length22.60567057
Min length3

Characters and Unicode

Total characters15360734
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDegrees Minutes Seconds
2nd rowDegrees Minutes Seconds
3rd rowDegrees Minutes Seconds
4th rowDegrees Minutes Seconds
5th rowDegrees Minutes Seconds
ValueCountFrequency (%)
degrees 670900
33.4%
minutes 648195
32.3%
seconds 648195
32.3%
decimal 22705
 
1.1%
township 7004
 
0.3%
range 7004
 
0.3%
marsden 605
 
< 0.1%
square 605
 
< 0.1%
unknown 532
 
< 0.1%
utm 464
 
< 0.1%
Other values (3) 6
 
< 0.1%
2025-01-07T10:46:10.327375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 3340010
21.7%
s 1974899
12.9%
1326707
 
8.6%
n 1312599
 
8.5%
i 677904
 
4.4%
g 677904
 
4.4%
r 672113
 
4.4%
d 671463
 
4.4%
D 670945
 
4.4%
c 670901
 
4.4%
Other values (20) 3365289
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15360734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3340010
21.7%
s 1974899
12.9%
1326707
 
8.6%
n 1312599
 
8.5%
i 677904
 
4.4%
g 677904
 
4.4%
r 672113
 
4.4%
d 671463
 
4.4%
D 670945
 
4.4%
c 670901
 
4.4%
Other values (20) 3365289
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15360734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3340010
21.7%
s 1974899
12.9%
1326707
 
8.6%
n 1312599
 
8.5%
i 677904
 
4.4%
g 677904
 
4.4%
r 672113
 
4.4%
d 671463
 
4.4%
D 670945
 
4.4%
c 670901
 
4.4%
Other values (20) 3365289
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15360734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3340010
21.7%
s 1974899
12.9%
1326707
 
8.6%
n 1312599
 
8.5%
i 677904
 
4.4%
g 677904
 
4.4%
r 672113
 
4.4%
d 671463
 
4.4%
D 670945
 
4.4%
c 670901
 
4.4%
Other values (20) 3365289
21.9%

verbatimSRS
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:10.374375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters20
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row1936-08-14
2nd row1926-08-24
ValueCountFrequency (%)
1936-08-14 1
50.0%
1926-08-24 1
50.0%
2025-01-07T10:46:10.469615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 4
20.0%
1 3
15.0%
9 2
10.0%
6 2
10.0%
0 2
10.0%
4 2
10.0%
8 2
10.0%
2 2
10.0%
3 1
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 4
20.0%
1 3
15.0%
9 2
10.0%
6 2
10.0%
0 2
10.0%
4 2
10.0%
8 2
10.0%
2 2
10.0%
3 1
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 4
20.0%
1 3
15.0%
9 2
10.0%
6 2
10.0%
0 2
10.0%
4 2
10.0%
8 2
10.0%
2 2
10.0%
3 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 4
20.0%
1 3
15.0%
9 2
10.0%
6 2
10.0%
0 2
10.0%
4 2
10.0%
8 2
10.0%
2 2
10.0%
3 1
 
5.0%

footprintWKT
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

footprintSRS
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean231.5
Minimum227
Maximum236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:10.522560image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum227
5-th percentile227.45
Q1229.25
median231.5
Q3233.75
95-th percentile235.55
Maximum236
Range9
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation6.363961031
Coefficient of variation (CV)0.02749011244
Kurtosisnan
Mean231.5
Median Absolute Deviation (MAD)4.5
Skewnessnan
Sum463
Variance40.5
MonotonicityStrictly increasing
2025-01-07T10:46:10.566559image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
227 1
 
< 0.1%
236 1
 
< 0.1%
(Missing) 1926391
> 99.9%
ValueCountFrequency (%)
227 1
< 0.1%
236 1
< 0.1%
ValueCountFrequency (%)
236 1
< 0.1%
227 1
< 0.1%

footprintSpatialFit
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean231.5
Minimum227
Maximum236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:10.610084image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum227
5-th percentile227.45
Q1229.25
median231.5
Q3233.75
95-th percentile235.55
Maximum236
Range9
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation6.363961031
Coefficient of variation (CV)0.02749011244
Kurtosisnan
Mean231.5
Median Absolute Deviation (MAD)4.5
Skewnessnan
Sum463
Variance40.5
MonotonicityStrictly increasing
2025-01-07T10:46:10.653589image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
227 1
 
< 0.1%
236 1
 
< 0.1%
(Missing) 1926391
> 99.9%
ValueCountFrequency (%)
227 1
< 0.1%
236 1
< 0.1%
ValueCountFrequency (%)
236 1
< 0.1%
227 1
< 0.1%

georeferencedBy
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1931
Minimum1926
Maximum1936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:10.699281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1926
5-th percentile1926.5
Q11928.5
median1931
Q31933.5
95-th percentile1935.5
Maximum1936
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.071067812
Coefficient of variation (CV)0.003661868365
Kurtosisnan
Mean1931
Median Absolute Deviation (MAD)5
Skewnessnan
Sum3862
Variance50
MonotonicityStrictly decreasing
2025-01-07T10:46:10.745787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1936 1
 
< 0.1%
1926 1
 
< 0.1%
(Missing) 1926391
> 99.9%
ValueCountFrequency (%)
1926 1
< 0.1%
1936 1
< 0.1%
ValueCountFrequency (%)
1936 1
< 0.1%
1926 1
< 0.1%

georeferencedDate
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing1926391
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean8
Minimum8
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:10.792344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum8
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosisnan
Mean8
Median Absolute Deviation (MAD)0
Skewnessnan
Sum16
Variance0
MonotonicityIncreasing
2025-01-07T10:46:10.834857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
8 2
 
< 0.1%
(Missing) 1926391
> 99.9%
ValueCountFrequency (%)
8 2
< 0.1%
ValueCountFrequency (%)
8 2
< 0.1%

georeferenceProtocol
Text

Missing 

Distinct115
Distinct (%)< 0.1%
Missing1265790
Missing (%)65.7%
Memory size14.7 MiB
2025-01-07T10:46:10.918654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length87
Median length20
Mean length20.10026748
Min length2

Characters and Unicode

Total characters13278297
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)< 0.1%

Sample

1st rowunknown, from legacy
2nd rowunknown, from legacy
3rd rowunknown, from legacy
4th rowunknown, from legacy
5th rowunknown, from legacy
ValueCountFrequency (%)
from 509060
26.2%
unknown 507577
26.1%
legacy 505126
26.0%
geolocate 70310
 
3.6%
names 41937
 
2.2%
geographic 41556
 
2.1%
of 35279
 
1.8%
getty 34687
 
1.8%
thesaurus 34686
 
1.8%
may 23191
 
1.2%
Other values (131) 141522
 
7.3%
2025-01-07T10:46:11.087032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 1560807
 
11.8%
1284328
 
9.7%
o 1253394
 
9.4%
e 822048
 
6.2%
a 797027
 
6.0%
r 642026
 
4.8%
c 624647
 
4.7%
g 591299
 
4.5%
u 580748
 
4.4%
y 577424
 
4.3%
Other values (54) 4544549
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13278297
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1560807
 
11.8%
1284328
 
9.7%
o 1253394
 
9.4%
e 822048
 
6.2%
a 797027
 
6.0%
r 642026
 
4.8%
c 624647
 
4.7%
g 591299
 
4.5%
u 580748
 
4.4%
y 577424
 
4.3%
Other values (54) 4544549
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13278297
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1560807
 
11.8%
1284328
 
9.7%
o 1253394
 
9.4%
e 822048
 
6.2%
a 797027
 
6.0%
r 642026
 
4.8%
c 624647
 
4.7%
g 591299
 
4.5%
u 580748
 
4.4%
y 577424
 
4.3%
Other values (54) 4544549
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13278297
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1560807
 
11.8%
1284328
 
9.7%
o 1253394
 
9.4%
e 822048
 
6.2%
a 797027
 
6.0%
r 642026
 
4.8%
c 624647
 
4.7%
g 591299
 
4.5%
u 580748
 
4.4%
y 577424
 
4.3%
Other values (54) 4544549
34.2%

georeferenceSources
Text

Missing 

Distinct2
Distinct (%)66.7%
Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:11.138541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length8
Mean length9.666666667
Min length8

Characters and Unicode

Total characters29
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st rowPARATYPE
2nd rowNORTH_AMERICA
3rd rowPARATYPE
ValueCountFrequency (%)
paratype 2
66.7%
north_america 1
33.3%
2025-01-07T10:46:11.241545image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 6
20.7%
P 4
13.8%
R 4
13.8%
T 3
10.3%
E 3
10.3%
Y 2
 
6.9%
N 1
 
3.4%
O 1
 
3.4%
H 1
 
3.4%
_ 1
 
3.4%
Other values (3) 3
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 6
20.7%
P 4
13.8%
R 4
13.8%
T 3
10.3%
E 3
10.3%
Y 2
 
6.9%
N 1
 
3.4%
O 1
 
3.4%
H 1
 
3.4%
_ 1
 
3.4%
Other values (3) 3
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 6
20.7%
P 4
13.8%
R 4
13.8%
T 3
10.3%
E 3
10.3%
Y 2
 
6.9%
N 1
 
3.4%
O 1
 
3.4%
H 1
 
3.4%
_ 1
 
3.4%
Other values (3) 3
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 6
20.7%
P 4
13.8%
R 4
13.8%
T 3
10.3%
E 3
10.3%
Y 2
 
6.9%
N 1
 
3.4%
O 1
 
3.4%
H 1
 
3.4%
_ 1
 
3.4%
Other values (3) 3
10.3%

georeferenceRemarks
Text

Missing 

Distinct4822
Distinct (%)15.9%
Missing1896105
Missing (%)98.4%
Memory size14.7 MiB
2025-01-07T10:46:11.436399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length122
Median length118
Mean length23.03717644
Min length1

Characters and Unicode

Total characters697750
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3165 ?
Unique (%)10.4%

Sample

1st rowExtended About 16 Km Offshore From Crystal River Power Plant
2nd row0.8 mile west of Montgomery-Polk county line, north side of
3rd rowSan Andreas Fault
4th row6 Mile W Of Watsonville
5th rowfrom Holt data card
ValueCountFrequency (%)
approximate 9789
 
8.9%
from 6478
 
5.9%
river 3464
 
3.2%
of 3097
 
2.8%
about 3076
 
2.8%
16 2974
 
2.7%
km 2970
 
2.7%
plant 2933
 
2.7%
offshore 2929
 
2.7%
power 2929
 
2.7%
Other values (4971) 68760
62.9%
2025-01-07T10:46:11.718863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
79111
 
11.3%
a 60517
 
8.7%
e 55652
 
8.0%
o 49194
 
7.1%
r 47507
 
6.8%
t 40249
 
5.8%
i 29470
 
4.2%
n 26681
 
3.8%
p 24672
 
3.5%
m 24234
 
3.5%
Other values (68) 260463
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 697750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
79111
 
11.3%
a 60517
 
8.7%
e 55652
 
8.0%
o 49194
 
7.1%
r 47507
 
6.8%
t 40249
 
5.8%
i 29470
 
4.2%
n 26681
 
3.8%
p 24672
 
3.5%
m 24234
 
3.5%
Other values (68) 260463
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 697750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
79111
 
11.3%
a 60517
 
8.7%
e 55652
 
8.0%
o 49194
 
7.1%
r 47507
 
6.8%
t 40249
 
5.8%
i 29470
 
4.2%
n 26681
 
3.8%
p 24672
 
3.5%
m 24234
 
3.5%
Other values (68) 260463
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 697750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
79111
 
11.3%
a 60517
 
8.7%
e 55652
 
8.0%
o 49194
 
7.1%
r 47507
 
6.8%
t 40249
 
5.8%
i 29470
 
4.2%
n 26681
 
3.8%
p 24672
 
3.5%
m 24234
 
3.5%
Other values (68) 260463
37.3%

geologicalContextID
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

earliestEonOrLowestEonothem
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

latestEonOrHighestEonothem
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing1926392
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:11.772861image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowUS
ValueCountFrequency (%)
us 1
100.0%
2025-01-07T10:46:11.864232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 1
50.0%
S 1
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 1
50.0%
S 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 1
50.0%
S 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 1
50.0%
S 1
50.0%

earliestEraOrLowestErathem
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing1926392
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:11.906544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowIdaho
ValueCountFrequency (%)
idaho 1
100.0%
2025-01-07T10:46:11.997533image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 1
20.0%
d 1
20.0%
a 1
20.0%
h 1
20.0%
o 1
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 1
20.0%
d 1
20.0%
a 1
20.0%
h 1
20.0%
o 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 1
20.0%
d 1
20.0%
a 1
20.0%
h 1
20.0%
o 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 1
20.0%
d 1
20.0%
a 1
20.0%
h 1
20.0%
o 1
20.0%

latestEraOrHighestErathem
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

earliestPeriodOrLowestSystem
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

latestPeriodOrHighestSystem
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

earliestEpochOrLowestSeries
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean4493591.5
Minimum2504455
Maximum6482728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:12.050040image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2504455
5-th percentile2703368.65
Q13499023.25
median4493591.5
Q35488159.75
95-th percentile6283814.35
Maximum6482728
Range3978273
Interquartile range (IQR)1989136.5

Descriptive statistics

Standard deviation2813063.816
Coefficient of variation (CV)0.6260168099
Kurtosisnan
Mean4493591.5
Median Absolute Deviation (MAD)1989136.5
Skewnessnan
Sum8987183
Variance7.913328031 × 1012
MonotonicityStrictly decreasing
2025-01-07T10:46:12.098849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
6482728 1
 
< 0.1%
2504455 1
 
< 0.1%
(Missing) 1926391
> 99.9%
ValueCountFrequency (%)
2504455 1
< 0.1%
6482728 1
< 0.1%
ValueCountFrequency (%)
6482728 1
< 0.1%
2504455 1
< 0.1%
Distinct3
Distinct (%)100.0%
Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:12.151354image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length75
Median length37
Mean length46.66666667
Min length28

Characters and Unicode

Total characters140
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowNorth America, North Pacific Ocean, Departure Bay, Canada, British Columbia
2nd rowNorth America, United States, Georgia
3rd rowNorth America, United States
ValueCountFrequency (%)
north 4
21.1%
america 3
15.8%
united 2
10.5%
states 2
10.5%
ocean 1
 
5.3%
pacific 1
 
5.3%
departure 1
 
5.3%
bay 1
 
5.3%
british 1
 
5.3%
canada 1
 
5.3%
Other values (2) 2
10.5%
2025-01-07T10:46:12.256371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16
 
11.4%
a 14
 
10.0%
t 12
 
8.6%
e 11
 
7.9%
i 11
 
7.9%
r 11
 
7.9%
, 7
 
5.0%
c 6
 
4.3%
o 6
 
4.3%
h 5
 
3.6%
Other values (21) 41
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16
 
11.4%
a 14
 
10.0%
t 12
 
8.6%
e 11
 
7.9%
i 11
 
7.9%
r 11
 
7.9%
, 7
 
5.0%
c 6
 
4.3%
o 6
 
4.3%
h 5
 
3.6%
Other values (21) 41
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16
 
11.4%
a 14
 
10.0%
t 12
 
8.6%
e 11
 
7.9%
i 11
 
7.9%
r 11
 
7.9%
, 7
 
5.0%
c 6
 
4.3%
o 6
 
4.3%
h 5
 
3.6%
Other values (21) 41
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16
 
11.4%
a 14
 
10.0%
t 12
 
8.6%
e 11
 
7.9%
i 11
 
7.9%
r 11
 
7.9%
, 7
 
5.0%
c 6
 
4.3%
o 6
 
4.3%
h 5
 
3.6%
Other values (21) 41
29.3%

earliestAgeOrLowestStage
Text

Constant  Missing 

Distinct1
Distinct (%)33.3%
Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:12.306914image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters39
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTH_AMERICA
2nd rowNORTH_AMERICA
3rd rowNORTH_AMERICA
ValueCountFrequency (%)
north_america 3
100.0%
2025-01-07T10:46:12.408412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 6
15.4%
A 6
15.4%
N 3
7.7%
O 3
7.7%
T 3
7.7%
H 3
7.7%
_ 3
7.7%
M 3
7.7%
E 3
7.7%
I 3
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 6
15.4%
A 6
15.4%
N 3
7.7%
O 3
7.7%
T 3
7.7%
H 3
7.7%
_ 3
7.7%
M 3
7.7%
E 3
7.7%
I 3
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 6
15.4%
A 6
15.4%
N 3
7.7%
O 3
7.7%
T 3
7.7%
H 3
7.7%
_ 3
7.7%
M 3
7.7%
E 3
7.7%
I 3
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 6
15.4%
A 6
15.4%
N 3
7.7%
O 3
7.7%
T 3
7.7%
H 3
7.7%
_ 3
7.7%
M 3
7.7%
E 3
7.7%
I 3
7.7%

latestAgeOrHighestStage
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing1926392
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:12.467687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length34
Median length34
Mean length34
Min length34

Characters and Unicode

Total characters34
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowNorth Pacific Ocean, Departure Bay
ValueCountFrequency (%)
north 1
20.0%
pacific 1
20.0%
ocean 1
20.0%
departure 1
20.0%
bay 1
20.0%
2025-01-07T10:46:12.571969image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4
 
11.8%
4
 
11.8%
r 3
 
8.8%
c 3
 
8.8%
e 3
 
8.8%
i 2
 
5.9%
t 2
 
5.9%
o 1
 
2.9%
N 1
 
2.9%
P 1
 
2.9%
Other values (10) 10
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4
 
11.8%
4
 
11.8%
r 3
 
8.8%
c 3
 
8.8%
e 3
 
8.8%
i 2
 
5.9%
t 2
 
5.9%
o 1
 
2.9%
N 1
 
2.9%
P 1
 
2.9%
Other values (10) 10
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4
 
11.8%
4
 
11.8%
r 3
 
8.8%
c 3
 
8.8%
e 3
 
8.8%
i 2
 
5.9%
t 2
 
5.9%
o 1
 
2.9%
N 1
 
2.9%
P 1
 
2.9%
Other values (10) 10
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4
 
11.8%
4
 
11.8%
r 3
 
8.8%
c 3
 
8.8%
e 3
 
8.8%
i 2
 
5.9%
t 2
 
5.9%
o 1
 
2.9%
N 1
 
2.9%
P 1
 
2.9%
Other values (10) 10
29.4%

lowestBiostratigraphicZone
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

highestBiostratigraphicZone
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB
Distinct4
Distinct (%)80.0%
Missing1926388
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:12.633520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length46
Median length2
Mean length18.8
Min length2

Characters and Unicode

Total characters94
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)60.0%

Sample

1st rowHemionchos striatus Campbell & Beveridge, 2006
2nd rowCA
3rd rowUS
4th rowUS
5th rowConspicuum icteridorum Denton & Byrd, 1951
ValueCountFrequency (%)
us 2
13.3%
2
13.3%
hemionchos 1
 
6.7%
striatus 1
 
6.7%
campbell 1
 
6.7%
beveridge 1
 
6.7%
2006 1
 
6.7%
ca 1
 
6.7%
conspicuum 1
 
6.7%
icteridorum 1
 
6.7%
Other values (3) 3
20.0%
2025-01-07T10:46:12.749736image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10
 
10.6%
e 7
 
7.4%
i 6
 
6.4%
r 5
 
5.3%
o 5
 
5.3%
n 4
 
4.3%
u 4
 
4.3%
t 4
 
4.3%
s 4
 
4.3%
m 4
 
4.3%
Other values (25) 41
43.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
10
 
10.6%
e 7
 
7.4%
i 6
 
6.4%
r 5
 
5.3%
o 5
 
5.3%
n 4
 
4.3%
u 4
 
4.3%
t 4
 
4.3%
s 4
 
4.3%
m 4
 
4.3%
Other values (25) 41
43.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
10
 
10.6%
e 7
 
7.4%
i 6
 
6.4%
r 5
 
5.3%
o 5
 
5.3%
n 4
 
4.3%
u 4
 
4.3%
t 4
 
4.3%
s 4
 
4.3%
m 4
 
4.3%
Other values (25) 41
43.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
10
 
10.6%
e 7
 
7.4%
i 6
 
6.4%
r 5
 
5.3%
o 5
 
5.3%
n 4
 
4.3%
u 4
 
4.3%
t 4
 
4.3%
s 4
 
4.3%
m 4
 
4.3%
Other values (25) 41
43.6%

group
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:12.796907image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length11.5
Mean length11.5
Min length7

Characters and Unicode

Total characters23
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowBritish Columbia
2nd rowGeorgia
ValueCountFrequency (%)
british 1
33.3%
columbia 1
33.3%
georgia 1
33.3%
2025-01-07T10:46:12.892742image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 4
17.4%
o 2
 
8.7%
r 2
 
8.7%
a 2
 
8.7%
B 1
 
4.3%
s 1
 
4.3%
h 1
 
4.3%
1
 
4.3%
t 1
 
4.3%
C 1
 
4.3%
Other values (7) 7
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 4
17.4%
o 2
 
8.7%
r 2
 
8.7%
a 2
 
8.7%
B 1
 
4.3%
s 1
 
4.3%
h 1
 
4.3%
1
 
4.3%
t 1
 
4.3%
C 1
 
4.3%
Other values (7) 7
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 4
17.4%
o 2
 
8.7%
r 2
 
8.7%
a 2
 
8.7%
B 1
 
4.3%
s 1
 
4.3%
h 1
 
4.3%
1
 
4.3%
t 1
 
4.3%
C 1
 
4.3%
Other values (7) 7
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 4
17.4%
o 2
 
8.7%
r 2
 
8.7%
a 2
 
8.7%
B 1
 
4.3%
s 1
 
4.3%
h 1
 
4.3%
1
 
4.3%
t 1
 
4.3%
C 1
 
4.3%
Other values (7) 7
30.4%

formation
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

member
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

bed
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing1926392
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:12.936249image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowMoultrie
ValueCountFrequency (%)
moultrie 1
100.0%
2025-01-07T10:46:13.026215image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 1
12.5%
o 1
12.5%
u 1
12.5%
l 1
12.5%
t 1
12.5%
r 1
12.5%
i 1
12.5%
e 1
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 1
12.5%
o 1
12.5%
u 1
12.5%
l 1
12.5%
t 1
12.5%
r 1
12.5%
i 1
12.5%
e 1
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 1
12.5%
o 1
12.5%
u 1
12.5%
l 1
12.5%
t 1
12.5%
r 1
12.5%
i 1
12.5%
e 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 1
12.5%
o 1
12.5%
u 1
12.5%
l 1
12.5%
t 1
12.5%
r 1
12.5%
i 1
12.5%
e 1
12.5%

identificationID
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

verbatimIdentification
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB
Distinct7
Distinct (%)< 0.1%
Missing1908260
Missing (%)99.1%
Memory size14.7 MiB
2025-01-07T10:46:13.075216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length76
Median length3
Mean length3.553796945
Min length3

Characters and Unicode

Total characters64441
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowcf.
2nd rowcf.
3rd rowuncertain
4th rowcf.
5th rowcf.
ValueCountFrequency (%)
cf 15638
86.2%
uncertain 1489
 
8.2%
aff 600
 
3.3%
near 404
 
2.2%
animalia 2
 
< 0.1%
platyhelminthes 2
 
< 0.1%
cestoda 1
 
< 0.1%
trematoda 1
 
< 0.1%
digenea 1
 
< 0.1%
plagiorchiida 1
 
< 0.1%
2025-01-07T10:46:13.285312image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 17130
26.6%
f 16838
26.1%
. 16238
25.2%
n 3387
 
5.3%
a 2506
 
3.9%
e 1903
 
3.0%
r 1896
 
2.9%
i 1502
 
2.3%
t 1495
 
2.3%
u 1487
 
2.3%
Other values (16) 59
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64441
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 17130
26.6%
f 16838
26.1%
. 16238
25.2%
n 3387
 
5.3%
a 2506
 
3.9%
e 1903
 
3.0%
r 1896
 
2.9%
i 1502
 
2.3%
t 1495
 
2.3%
u 1487
 
2.3%
Other values (16) 59
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64441
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 17130
26.6%
f 16838
26.1%
. 16238
25.2%
n 3387
 
5.3%
a 2506
 
3.9%
e 1903
 
3.0%
r 1896
 
2.9%
i 1502
 
2.3%
t 1495
 
2.3%
u 1487
 
2.3%
Other values (16) 59
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64441
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 17130
26.6%
f 16838
26.1%
. 16238
25.2%
n 3387
 
5.3%
a 2506
 
3.9%
e 1903
 
3.0%
r 1896
 
2.9%
i 1502
 
2.3%
t 1495
 
2.3%
u 1487
 
2.3%
Other values (16) 59
 
0.1%

typeStatus
Text

Missing 

Distinct11
Distinct (%)< 0.1%
Missing1841066
Missing (%)95.6%
Memory size14.7 MiB
2025-01-07T10:46:13.336821image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length8
Mean length7.724987401
Min length4

Characters and Unicode

Total characters659150
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARATYPE
2nd rowHOLOTYPE
3rd rowPARATYPE
4th rowHOLOTYPE
5th rowPARATYPE
ValueCountFrequency (%)
paratype 40578
47.6%
holotype 25358
29.7%
syntype 9555
 
11.2%
type 4807
 
5.6%
allotype 2818
 
3.3%
lectotype 862
 
1.0%
paralectotype 795
 
0.9%
neotype 294
 
0.3%
hapantotype 242
 
0.3%
paraneotype 16
 
< 0.1%
2025-01-07T10:46:13.437802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 126956
19.3%
Y 94880
14.4%
E 87292
13.2%
T 87224
13.2%
A 86082
13.1%
O 55743
8.5%
R 41389
 
6.3%
L 32651
 
5.0%
H 25600
 
3.9%
N 10107
 
1.5%
Other values (7) 11226
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 659150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 126956
19.3%
Y 94880
14.4%
E 87292
13.2%
T 87224
13.2%
A 86082
13.1%
O 55743
8.5%
R 41389
 
6.3%
L 32651
 
5.0%
H 25600
 
3.9%
N 10107
 
1.5%
Other values (7) 11226
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 659150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 126956
19.3%
Y 94880
14.4%
E 87292
13.2%
T 87224
13.2%
A 86082
13.1%
O 55743
8.5%
R 41389
 
6.3%
L 32651
 
5.0%
H 25600
 
3.9%
N 10107
 
1.5%
Other values (7) 11226
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 659150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 126956
19.3%
Y 94880
14.4%
E 87292
13.2%
T 87224
13.2%
A 86082
13.1%
O 55743
8.5%
R 41389
 
6.3%
L 32651
 
5.0%
H 25600
 
3.9%
N 10107
 
1.5%
Other values (7) 11226
 
1.7%

identifiedBy
Text

Missing 

Distinct13461
Distinct (%)1.6%
Missing1085208
Missing (%)56.3%
Memory size14.7 MiB
2025-01-07T10:46:13.647600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length226
Median length133
Mean length38.24106825
Min length2

Characters and Unicode

Total characters32167813
Distinct characters94
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4200 ?
Unique (%)0.5%

Sample

1st rowOpresko, Dennis M., Oak Ridge National Laboratory (UNITED STATES)
2nd rowNance
3rd rowMah, Christopher, (IZ), Smithsonian Institution - National Museum of Natural History (UNITED STATES)
4th rowVerrill, Addison E., Peabody Museum, Yale
5th rowJudkins, D.
ValueCountFrequency (%)
of 247193
 
5.3%
museum 200643
 
4.3%
national 197127
 
4.2%
institution 188591
 
4.1%
smithsonian 186061
 
4.0%
natural 185777
 
4.0%
history 185423
 
4.0%
united 130413
 
2.8%
states 129643
 
2.8%
87200
 
1.9%
Other values (9433) 2904278
62.6%
2025-01-07T10:46:13.926067image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3801164
 
11.8%
a 2080528
 
6.5%
i 2056250
 
6.4%
t 2013216
 
6.3%
n 1896071
 
5.9%
o 1744817
 
5.4%
e 1500120
 
4.7%
r 1384928
 
4.3%
s 1382760
 
4.3%
, 1349377
 
4.2%
Other values (84) 12958582
40.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32167813
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3801164
 
11.8%
a 2080528
 
6.5%
i 2056250
 
6.4%
t 2013216
 
6.3%
n 1896071
 
5.9%
o 1744817
 
5.4%
e 1500120
 
4.7%
r 1384928
 
4.3%
s 1382760
 
4.3%
, 1349377
 
4.2%
Other values (84) 12958582
40.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32167813
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3801164
 
11.8%
a 2080528
 
6.5%
i 2056250
 
6.4%
t 2013216
 
6.3%
n 1896071
 
5.9%
o 1744817
 
5.4%
e 1500120
 
4.7%
r 1384928
 
4.3%
s 1382760
 
4.3%
, 1349377
 
4.2%
Other values (84) 12958582
40.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32167813
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3801164
 
11.8%
a 2080528
 
6.5%
i 2056250
 
6.4%
t 2013216
 
6.3%
n 1896071
 
5.9%
o 1744817
 
5.4%
e 1500120
 
4.7%
r 1384928
 
4.3%
s 1382760
 
4.3%
, 1349377
 
4.2%
Other values (84) 12958582
40.3%

identifiedByID
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:13.980531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8
Min length7

Characters and Unicode

Total characters16
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowCestoda
2nd rowTrematoda
ValueCountFrequency (%)
cestoda 1
50.0%
trematoda 1
50.0%
2025-01-07T10:46:14.086181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3
18.8%
e 2
12.5%
d 2
12.5%
t 2
12.5%
o 2
12.5%
C 1
 
6.2%
s 1
 
6.2%
T 1
 
6.2%
r 1
 
6.2%
m 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3
18.8%
e 2
12.5%
d 2
12.5%
t 2
12.5%
o 2
12.5%
C 1
 
6.2%
s 1
 
6.2%
T 1
 
6.2%
r 1
 
6.2%
m 1
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3
18.8%
e 2
12.5%
d 2
12.5%
t 2
12.5%
o 2
12.5%
C 1
 
6.2%
s 1
 
6.2%
T 1
 
6.2%
r 1
 
6.2%
m 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3
18.8%
e 2
12.5%
d 2
12.5%
t 2
12.5%
o 2
12.5%
C 1
 
6.2%
s 1
 
6.2%
T 1
 
6.2%
r 1
 
6.2%
m 1
 
6.2%

dateIdentified
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:14.135451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length13.5
Mean length13.5
Min length13

Characters and Unicode

Total characters27
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowTrypanorhyncha
2nd rowPlagiorchiida
ValueCountFrequency (%)
trypanorhyncha 1
50.0%
plagiorchiida 1
50.0%
2025-01-07T10:46:14.235647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4
14.8%
r 3
11.1%
i 3
11.1%
h 3
11.1%
n 2
7.4%
y 2
7.4%
c 2
7.4%
o 2
7.4%
p 1
 
3.7%
T 1
 
3.7%
Other values (4) 4
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4
14.8%
r 3
11.1%
i 3
11.1%
h 3
11.1%
n 2
7.4%
y 2
7.4%
c 2
7.4%
o 2
7.4%
p 1
 
3.7%
T 1
 
3.7%
Other values (4) 4
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4
14.8%
r 3
11.1%
i 3
11.1%
h 3
11.1%
n 2
7.4%
y 2
7.4%
c 2
7.4%
o 2
7.4%
p 1
 
3.7%
T 1
 
3.7%
Other values (4) 4
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4
14.8%
r 3
11.1%
i 3
11.1%
h 3
11.1%
n 2
7.4%
y 2
7.4%
c 2
7.4%
o 2
7.4%
p 1
 
3.7%
T 1
 
3.7%
Other values (4) 4
14.8%

identificationReferences
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

identificationVerificationStatus
Unsupported

Missing  Rejected  Unsupported 

Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB

identificationRemarks
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing1926392
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean-83.7685
Minimum-83.7685
Maximum-83.7685
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size14.7 MiB
2025-01-07T10:46:14.290625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-83.7685
5-th percentile-83.7685
Q1-83.7685
median-83.7685
Q3-83.7685
95-th percentile-83.7685
Maximum-83.7685
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean-83.7685
Median Absolute Deviation (MAD)0
Skewnessnan
Sum-83.7685
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:46:14.334133image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
-83.7685 1
 
< 0.1%
(Missing) 1926392
> 99.9%
ValueCountFrequency (%)
-83.7685 1
< 0.1%
ValueCountFrequency (%)
-83.7685 1
< 0.1%

taxonID
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

scientificNameID
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

acceptedNameUsageID
Unsupported

Rejected  Unsupported 

Missing2069
Missing (%)0.1%
Memory size14.7 MiB

parentNameUsageID
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:14.370134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters20
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowHemionchos
2nd rowConspicuum
ValueCountFrequency (%)
hemionchos 1
50.0%
conspicuum 1
50.0%
2025-01-07T10:46:14.463310image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3
15.0%
i 2
10.0%
n 2
10.0%
m 2
10.0%
u 2
10.0%
s 2
10.0%
c 2
10.0%
e 1
 
5.0%
H 1
 
5.0%
h 1
 
5.0%
Other values (2) 2
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3
15.0%
i 2
10.0%
n 2
10.0%
m 2
10.0%
u 2
10.0%
s 2
10.0%
c 2
10.0%
e 1
 
5.0%
H 1
 
5.0%
h 1
 
5.0%
Other values (2) 2
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3
15.0%
i 2
10.0%
n 2
10.0%
m 2
10.0%
u 2
10.0%
s 2
10.0%
c 2
10.0%
e 1
 
5.0%
H 1
 
5.0%
h 1
 
5.0%
Other values (2) 2
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3
15.0%
i 2
10.0%
n 2
10.0%
m 2
10.0%
u 2
10.0%
s 2
10.0%
c 2
10.0%
e 1
 
5.0%
H 1
 
5.0%
h 1
 
5.0%
Other values (2) 2
10.0%

originalNameUsageID
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

nameAccordingToID
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

namePublishedInID
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:14.509798image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length9.5
Mean length9.5
Min length8

Characters and Unicode

Total characters19
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowstriatus
2nd rowicteridorum
ValueCountFrequency (%)
striatus 1
50.0%
icteridorum 1
50.0%
2025-01-07T10:46:14.611883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 3
15.8%
r 3
15.8%
i 3
15.8%
s 2
10.5%
u 2
10.5%
a 1
 
5.3%
c 1
 
5.3%
e 1
 
5.3%
d 1
 
5.3%
o 1
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 3
15.8%
r 3
15.8%
i 3
15.8%
s 2
10.5%
u 2
10.5%
a 1
 
5.3%
c 1
 
5.3%
e 1
 
5.3%
d 1
 
5.3%
o 1
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 3
15.8%
r 3
15.8%
i 3
15.8%
s 2
10.5%
u 2
10.5%
a 1
 
5.3%
c 1
 
5.3%
e 1
 
5.3%
d 1
 
5.3%
o 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 3
15.8%
r 3
15.8%
i 3
15.8%
s 2
10.5%
u 2
10.5%
a 1
 
5.3%
c 1
 
5.3%
e 1
 
5.3%
d 1
 
5.3%
o 1
 
5.3%

taxonConceptID
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB
Distinct113079
Distinct (%)5.9%
Missing6
Missing (%)< 0.1%
Memory size14.7 MiB
2025-01-07T10:46:14.835211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length168
Median length102
Mean length29.16433821
Min length5

Characters and Unicode

Total characters56181802
Distinct characters116
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38721 ?
Unique (%)2.0%

Sample

1st rowScypha Gray, 1821
2nd rowBulla striata Bruguière, 1792
3rd rowStylopathes columnaris (Duchassaing, 1870)
4th rowOphiothrix suensonii Lütken, 1856
5th rowCypraea labrolineata Gaskoin, 1849
ValueCountFrequency (%)
136410
 
2.0%
linnaeus 96753
 
1.4%
1758 81495
 
1.2%
say 50998
 
0.8%
lamarck 40009
 
0.6%
dall 28184
 
0.4%
conus 24224
 
0.4%
gastropoda 23786
 
0.4%
1791 23649
 
0.3%
gmelin 23215
 
0.3%
Other values (70965) 6239236
92.2%
2025-01-07T10:46:15.147460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4939373
 
8.8%
4841572
 
8.6%
i 3725884
 
6.6%
e 3410133
 
6.1%
r 2844813
 
5.1%
s 2669041
 
4.8%
o 2472444
 
4.4%
l 2451221
 
4.4%
n 2432205
 
4.3%
t 1939529
 
3.5%
Other values (106) 24455587
43.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56181802
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4939373
 
8.8%
4841572
 
8.6%
i 3725884
 
6.6%
e 3410133
 
6.1%
r 2844813
 
5.1%
s 2669041
 
4.8%
o 2472444
 
4.4%
l 2451221
 
4.4%
n 2432205
 
4.3%
t 1939529
 
3.5%
Other values (106) 24455587
43.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56181802
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4939373
 
8.8%
4841572
 
8.6%
i 3725884
 
6.6%
e 3410133
 
6.1%
r 2844813
 
5.1%
s 2669041
 
4.8%
o 2472444
 
4.4%
l 2451221
 
4.4%
n 2432205
 
4.3%
t 1939529
 
3.5%
Other values (106) 24455587
43.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56181802
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4939373
 
8.8%
4841572
 
8.6%
i 3725884
 
6.6%
e 3410133
 
6.1%
r 2844813
 
5.1%
s 2669041
 
4.8%
o 2472444
 
4.4%
l 2451221
 
4.4%
n 2432205
 
4.3%
t 1939529
 
3.5%
Other values (106) 24455587
43.5%

acceptedNameUsage
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing1926391
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:15.199032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters14
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSPECIES
2nd rowSPECIES
ValueCountFrequency (%)
species 2
100.0%
2025-01-07T10:46:15.286901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 4
28.6%
E 4
28.6%
P 2
14.3%
C 2
14.3%
I 2
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 4
28.6%
E 4
28.6%
P 2
14.3%
C 2
14.3%
I 2
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 4
28.6%
E 4
28.6%
P 2
14.3%
C 2
14.3%
I 2
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 4
28.6%
E 4
28.6%
P 2
14.3%
C 2
14.3%
I 2
14.3%

parentNameUsage
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing1926392
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:15.329407image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters9
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowGEOLocate
ValueCountFrequency (%)
geolocate 1
100.0%
2025-01-07T10:46:15.419493image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 1
11.1%
E 1
11.1%
O 1
11.1%
L 1
11.1%
o 1
11.1%
c 1
11.1%
a 1
11.1%
t 1
11.1%
e 1
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 1
11.1%
E 1
11.1%
O 1
11.1%
L 1
11.1%
o 1
11.1%
c 1
11.1%
a 1
11.1%
t 1
11.1%
e 1
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 1
11.1%
E 1
11.1%
O 1
11.1%
L 1
11.1%
o 1
11.1%
c 1
11.1%
a 1
11.1%
t 1
11.1%
e 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 1
11.1%
E 1
11.1%
O 1
11.1%
L 1
11.1%
o 1
11.1%
c 1
11.1%
a 1
11.1%
t 1
11.1%
e 1
11.1%

originalNameUsage
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

nameAccordingTo
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

namePublishedIn
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing1926391
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:15.461494image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters16
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowACCEPTED
2nd rowACCEPTED
ValueCountFrequency (%)
accepted 2
100.0%
2025-01-07T10:46:15.550117image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 4
25.0%
E 4
25.0%
A 2
12.5%
P 2
12.5%
T 2
12.5%
D 2
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 4
25.0%
E 4
25.0%
A 2
12.5%
P 2
12.5%
T 2
12.5%
D 2
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 4
25.0%
E 4
25.0%
A 2
12.5%
P 2
12.5%
T 2
12.5%
D 2
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 4
25.0%
E 4
25.0%
A 2
12.5%
P 2
12.5%
T 2
12.5%
D 2
12.5%

namePublishedInYear
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB
Distinct4354
Distinct (%)0.2%
Missing469
Missing (%)< 0.1%
Memory size14.7 MiB
2025-01-07T10:46:15.705199image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length134
Median length117
Mean length62.96739176
Min length7

Characters and Unicode

Total characters121270411
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique586 ?
Unique (%)< 0.1%

Sample

1st rowAnimalia, Porifera, Calcarea
2nd rowAnimalia, Mollusca, Gastropoda, Bullidae
3rd rowAnimalia, Cnidaria, Anthozoa, Hexacorallia, Antipatharia, Stylopathidae
4th rowAnimalia, Echinodermata, Ophiuroidea, Ophiurida, Ophiotrichidae
5th rowAnimalia, Mollusca, Gastropoda, Cypraeidae
ValueCountFrequency (%)
animalia 1922044
 
18.1%
mollusca 866407
 
8.1%
gastropoda 612759
 
5.8%
arthropoda 390750
 
3.7%
crustacea 385110
 
3.6%
malacostraca 301975
 
2.8%
eumalacostraca 294895
 
2.8%
annelida 241801
 
2.3%
polychaeta 212969
 
2.0%
bivalvia 207685
 
2.0%
Other values (4342) 5202802
48.9%
2025-01-07T10:46:15.945859image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 19360559
16.0%
i 10629446
 
8.8%
8713273
 
7.2%
, 8691731
 
7.2%
o 7923783
 
6.5%
l 7526240
 
6.2%
e 6162876
 
5.1%
d 5675251
 
4.7%
r 5612652
 
4.6%
c 5023755
 
4.1%
Other values (50) 35950845
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121270411
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 19360559
16.0%
i 10629446
 
8.8%
8713273
 
7.2%
, 8691731
 
7.2%
o 7923783
 
6.5%
l 7526240
 
6.2%
e 6162876
 
5.1%
d 5675251
 
4.7%
r 5612652
 
4.6%
c 5023755
 
4.1%
Other values (50) 35950845
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121270411
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 19360559
16.0%
i 10629446
 
8.8%
8713273
 
7.2%
, 8691731
 
7.2%
o 7923783
 
6.5%
l 7526240
 
6.2%
e 6162876
 
5.1%
d 5675251
 
4.7%
r 5612652
 
4.6%
c 5023755
 
4.1%
Other values (50) 35950845
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121270411
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 19360559
16.0%
i 10629446
 
8.8%
8713273
 
7.2%
, 8691731
 
7.2%
o 7923783
 
6.5%
l 7526240
 
6.2%
e 6162876
 
5.1%
d 5675251
 
4.7%
r 5612652
 
4.6%
c 5023755
 
4.1%
Other values (50) 35950845
29.6%
Distinct6
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size14.7 MiB
2025-01-07T10:46:16.009113image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length8
Mean length8.007927786
Min length8

Characters and Unicode

Total characters15426384
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnimalia
2nd rowAnimalia
3rd rowAnimalia
4th rowAnimalia
5th rowAnimalia
ValueCountFrequency (%)
animalia 1920497
99.6%
chromista 2826
 
0.1%
incertae 2065
 
0.1%
sedis 2065
 
0.1%
protozoa 964
 
< 0.1%
bacteria 35
 
< 0.1%
821cc27a-e3bb-4bc5-ac34-89ada245069d 2
 
< 0.1%
2025-01-07T10:46:16.112393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 3847985
24.9%
a 3846927
24.9%
m 1923323
12.5%
n 1922562
12.5%
A 1920497
12.4%
l 1920497
12.4%
s 6956
 
< 0.1%
e 6232
 
< 0.1%
t 5890
 
< 0.1%
r 5890
 
< 0.1%
Other values (21) 19625
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15426384
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 3847985
24.9%
a 3846927
24.9%
m 1923323
12.5%
n 1922562
12.5%
A 1920497
12.4%
l 1920497
12.4%
s 6956
 
< 0.1%
e 6232
 
< 0.1%
t 5890
 
< 0.1%
r 5890
 
< 0.1%
Other values (21) 19625
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15426384
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 3847985
24.9%
a 3846927
24.9%
m 1923323
12.5%
n 1922562
12.5%
A 1920497
12.4%
l 1920497
12.4%
s 6956
 
< 0.1%
e 6232
 
< 0.1%
t 5890
 
< 0.1%
r 5890
 
< 0.1%
Other values (21) 19625
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15426384
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 3847985
24.9%
a 3846927
24.9%
m 1923323
12.5%
n 1922562
12.5%
A 1920497
12.4%
l 1920497
12.4%
s 6956
 
< 0.1%
e 6232
 
< 0.1%
t 5890
 
< 0.1%
r 5890
 
< 0.1%
Other values (21) 19625
 
0.1%

phylum
Text

Distinct52
Distinct (%)< 0.1%
Missing3160
Missing (%)0.2%
Memory size14.7 MiB
2025-01-07T10:46:16.168888image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length8
Mean length8.850655641
Min length2

Characters and Unicode

Total characters17021873
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowPorifera
2nd rowMollusca
3rd rowCnidaria
4th rowEchinodermata
5th rowMollusca
ValueCountFrequency (%)
mollusca 864192
44.9%
arthropoda 392999
20.4%
annelida 241615
 
12.6%
cnidaria 117703
 
6.1%
echinodermata 91212
 
4.7%
nematoda 68758
 
3.6%
platyhelminthes 45840
 
2.4%
porifera 32733
 
1.7%
chordata 19745
 
1.0%
sipuncula 10415
 
0.5%
Other values (42) 38021
 
2.0%
2025-01-07T10:46:16.294764image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2238532
13.2%
l 2078076
12.2%
o 1907515
11.2%
r 1110893
 
6.5%
c 984617
 
5.8%
d 936895
 
5.5%
s 910659
 
5.3%
u 885746
 
5.2%
M 866329
 
5.1%
n 769092
 
4.5%
Other values (30) 4333519
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17021873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2238532
13.2%
l 2078076
12.2%
o 1907515
11.2%
r 1110893
 
6.5%
c 984617
 
5.8%
d 936895
 
5.5%
s 910659
 
5.3%
u 885746
 
5.2%
M 866329
 
5.1%
n 769092
 
4.5%
Other values (30) 4333519
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17021873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2238532
13.2%
l 2078076
12.2%
o 1907515
11.2%
r 1110893
 
6.5%
c 984617
 
5.8%
d 936895
 
5.5%
s 910659
 
5.3%
u 885746
 
5.2%
M 866329
 
5.1%
n 769092
 
4.5%
Other values (30) 4333519
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17021873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2238532
13.2%
l 2078076
12.2%
o 1907515
11.2%
r 1110893
 
6.5%
c 984617
 
5.8%
d 936895
 
5.5%
s 910659
 
5.3%
u 885746
 
5.2%
M 866329
 
5.1%
n 769092
 
4.5%
Other values (30) 4333519
25.5%

class
Text

Missing 

Distinct116
Distinct (%)< 0.1%
Missing66157
Missing (%)3.4%
Memory size14.7 MiB
2025-01-07T10:46:16.393330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length19
Mean length10.05340075
Min length4

Characters and Unicode

Total characters18701698
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowCalcarea
2nd rowGastropoda
3rd rowAnthozoa
4th rowOphiuroidea
5th rowGastropoda
ValueCountFrequency (%)
gastropoda 610123
32.8%
malacostraca 301912
16.2%
polychaeta 211086
 
11.3%
bivalvia 207854
 
11.2%
anthozoa 93050
 
5.0%
copepoda 46190
 
2.5%
chromadorea 42750
 
2.3%
clitellata 30336
 
1.6%
ophiuroidea 27087
 
1.5%
asteroidea 25635
 
1.4%
Other values (106) 264213
14.2%
2025-01-07T10:46:16.560825image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4042336
21.6%
o 2534615
13.6%
t 1401870
 
7.5%
r 1169735
 
6.3%
s 1022238
 
5.5%
d 956343
 
5.1%
c 944030
 
5.0%
l 924665
 
4.9%
p 848962
 
4.5%
i 703184
 
3.8%
Other values (44) 4153720
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18701698
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4042336
21.6%
o 2534615
13.6%
t 1401870
 
7.5%
r 1169735
 
6.3%
s 1022238
 
5.5%
d 956343
 
5.1%
c 944030
 
5.0%
l 924665
 
4.9%
p 848962
 
4.5%
i 703184
 
3.8%
Other values (44) 4153720
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18701698
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4042336
21.6%
o 2534615
13.6%
t 1401870
 
7.5%
r 1169735
 
6.3%
s 1022238
 
5.5%
d 956343
 
5.1%
c 944030
 
5.0%
l 924665
 
4.9%
p 848962
 
4.5%
i 703184
 
3.8%
Other values (44) 4153720
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18701698
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4042336
21.6%
o 2534615
13.6%
t 1401870
 
7.5%
r 1169735
 
6.3%
s 1022238
 
5.5%
d 956343
 
5.1%
c 944030
 
5.0%
l 924665
 
4.9%
p 848962
 
4.5%
i 703184
 
3.8%
Other values (44) 4153720
22.2%

order
Text

Missing 

Distinct414
Distinct (%)< 0.1%
Missing329537
Missing (%)17.1%
Memory size14.7 MiB
2025-01-07T10:46:16.707275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length20
Mean length11.19175304
Min length5

Characters and Unicode

Total characters17871618
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)< 0.1%

Sample

1st rowLeucosolenida
2nd rowCephalaspidea
3rd rowAntipatharia
4th rowAmphilepidida
5th rowLittorinimorpha
ValueCountFrequency (%)
decapoda 196384
 
12.3%
neogastropoda 156428
 
9.8%
stylommatophora 116401
 
7.3%
littorinimorpha 113553
 
7.1%
phyllodocida 69439
 
4.3%
scleractinia 54200
 
3.4%
amphipoda 49533
 
3.1%
rhabditida 35176
 
2.2%
venerida 31275
 
2.0%
cardiida 30439
 
1.9%
Other values (404) 744028
46.6%
2025-01-07T10:46:17.005513image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2716551
15.2%
o 2130021
11.9%
i 1739041
 
9.7%
d 1413506
 
7.9%
t 1052242
 
5.9%
p 961716
 
5.4%
r 907952
 
5.1%
e 872746
 
4.9%
c 825635
 
4.6%
l 796133
 
4.5%
Other values (36) 4456075
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17871618
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2716551
15.2%
o 2130021
11.9%
i 1739041
 
9.7%
d 1413506
 
7.9%
t 1052242
 
5.9%
p 961716
 
5.4%
r 907952
 
5.1%
e 872746
 
4.9%
c 825635
 
4.6%
l 796133
 
4.5%
Other values (36) 4456075
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17871618
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2716551
15.2%
o 2130021
11.9%
i 1739041
 
9.7%
d 1413506
 
7.9%
t 1052242
 
5.9%
p 961716
 
5.4%
r 907952
 
5.1%
e 872746
 
4.9%
c 825635
 
4.6%
l 796133
 
4.5%
Other values (36) 4456075
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17871618
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2716551
15.2%
o 2130021
11.9%
i 1739041
 
9.7%
d 1413506
 
7.9%
t 1052242
 
5.9%
p 961716
 
5.4%
r 907952
 
5.1%
e 872746
 
4.9%
c 825635
 
4.6%
l 796133
 
4.5%
Other values (36) 4456075
24.9%

superfamily
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

family
Text

Missing 

Distinct3522
Distinct (%)0.2%
Missing144488
Missing (%)7.5%
Memory size14.7 MiB
2025-01-07T10:46:17.199536image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length21
Mean length11.20729837
Min length6

Characters and Unicode

Total characters19970341
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique272 ?
Unique (%)< 0.1%

Sample

1st rowSyconidae
2nd rowBullidae
3rd rowStylopathidae
4th rowOphiotrichidae
5th rowCypraeidae
ValueCountFrequency (%)
cambaridae 28956
 
1.6%
conidae 28425
 
1.6%
unionidae 26787
 
1.5%
muricidae 22783
 
1.3%
veneridae 18640
 
1.0%
cypraeidae 16831
 
0.9%
cerithiidae 16777
 
0.9%
spionidae 15856
 
0.9%
syllidae 14115
 
0.8%
pectinidae 12961
 
0.7%
Other values (3512) 1579774
88.7%
2025-01-07T10:46:17.456732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 2971768
14.9%
a 2739603
13.7%
e 2656666
13.3%
d 2019505
10.1%
o 1034346
 
5.2%
l 1016313
 
5.1%
r 1015168
 
5.1%
n 842574
 
4.2%
t 674978
 
3.4%
c 545975
 
2.7%
Other values (42) 4453445
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19970341
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2971768
14.9%
a 2739603
13.7%
e 2656666
13.3%
d 2019505
10.1%
o 1034346
 
5.2%
l 1016313
 
5.1%
r 1015168
 
5.1%
n 842574
 
4.2%
t 674978
 
3.4%
c 545975
 
2.7%
Other values (42) 4453445
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19970341
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2971768
14.9%
a 2739603
13.7%
e 2656666
13.3%
d 2019505
10.1%
o 1034346
 
5.2%
l 1016313
 
5.1%
r 1015168
 
5.1%
n 842574
 
4.2%
t 674978
 
3.4%
c 545975
 
2.7%
Other values (42) 4453445
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19970341
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2971768
14.9%
a 2739603
13.7%
e 2656666
13.3%
d 2019505
10.1%
o 1034346
 
5.2%
l 1016313
 
5.1%
r 1015168
 
5.1%
n 842574
 
4.2%
t 674978
 
3.4%
c 545975
 
2.7%
Other values (42) 4453445
22.3%

subfamily
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

tribe
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

subtribe
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:17.537436image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length130
Median length89
Mean length89
Min length48

Characters and Unicode

Total characters178
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT;GEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED_FROM_COORDINATES;CONTINENT_INVALID
2nd rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
ValueCountFrequency (%)
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;continent_derived_from_coordinates;continent_invalid 1
50.0%
occurrence_status_inferred_from_individual_count 1
50.0%
2025-01-07T10:46:17.655602image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 17
9.6%
N 16
 
9.0%
E 16
 
9.0%
I 15
 
8.4%
R 13
 
7.3%
D 13
 
7.3%
T 13
 
7.3%
O 12
 
6.7%
C 12
 
6.7%
U 10
 
5.6%
Other values (11) 41
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 17
9.6%
N 16
 
9.0%
E 16
 
9.0%
I 15
 
8.4%
R 13
 
7.3%
D 13
 
7.3%
T 13
 
7.3%
O 12
 
6.7%
C 12
 
6.7%
U 10
 
5.6%
Other values (11) 41
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 17
9.6%
N 16
 
9.0%
E 16
 
9.0%
I 15
 
8.4%
R 13
 
7.3%
D 13
 
7.3%
T 13
 
7.3%
O 12
 
6.7%
C 12
 
6.7%
U 10
 
5.6%
Other values (11) 41
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 17
9.6%
N 16
 
9.0%
E 16
 
9.0%
I 15
 
8.4%
R 13
 
7.3%
D 13
 
7.3%
T 13
 
7.3%
O 12
 
6.7%
C 12
 
6.7%
U 10
 
5.6%
Other values (11) 41
23.0%

genus
Text

Missing 

Distinct20787
Distinct (%)1.3%
Missing358044
Missing (%)18.6%
Memory size14.7 MiB
2025-01-07T10:46:17.854860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length27
Median length23
Mean length9.482777111
Min length2

Characters and Unicode

Total characters14872304
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3152 ?
Unique (%)0.2%

Sample

1st rowSycon
2nd rowBulla
3rd rowStylopathes
4th rowOphiothrix
5th rowNaria
ValueCountFrequency (%)
conus 22884
 
1.5%
cerithium 8956
 
0.6%
cambarus 8948
 
0.6%
faxonius 8189
 
0.5%
procambarus 8096
 
0.5%
aricidea 5223
 
0.3%
nerita 4536
 
0.3%
nassarius 4534
 
0.3%
pagurus 4234
 
0.3%
elimia 4085
 
0.3%
Other values (20777) 1488664
94.9%
2025-01-07T10:46:18.128213image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1794417
 
12.1%
i 1296042
 
8.7%
o 1190619
 
8.0%
e 1030226
 
6.9%
r 967232
 
6.5%
l 958555
 
6.4%
s 949415
 
6.4%
n 726098
 
4.9%
t 714218
 
4.8%
u 705352
 
4.7%
Other values (42) 4540130
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14872304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1794417
 
12.1%
i 1296042
 
8.7%
o 1190619
 
8.0%
e 1030226
 
6.9%
r 967232
 
6.5%
l 958555
 
6.4%
s 949415
 
6.4%
n 726098
 
4.9%
t 714218
 
4.8%
u 705352
 
4.7%
Other values (42) 4540130
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14872304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1794417
 
12.1%
i 1296042
 
8.7%
o 1190619
 
8.0%
e 1030226
 
6.9%
r 967232
 
6.5%
l 958555
 
6.4%
s 949415
 
6.4%
n 726098
 
4.9%
t 714218
 
4.8%
u 705352
 
4.7%
Other values (42) 4540130
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14872304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1794417
 
12.1%
i 1296042
 
8.7%
o 1190619
 
8.0%
e 1030226
 
6.9%
r 967232
 
6.5%
l 958555
 
6.4%
s 949415
 
6.4%
n 726098
 
4.9%
t 714218
 
4.8%
u 705352
 
4.7%
Other values (42) 4540130
30.5%

genericName
Text

Missing 

Distinct21084
Distinct (%)1.3%
Missing358043
Missing (%)18.6%
Memory size14.7 MiB
2025-01-07T10:46:18.353957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length27
Median length23
Mean length9.309154844
Min length1

Characters and Unicode

Total characters14600013
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3830 ?
Unique (%)0.2%

Sample

1st rowScypha
2nd rowBulla
3rd rowStylopathes
4th rowOphiothrix
5th rowCypraea
ValueCountFrequency (%)
conus 24156
 
1.5%
cypraea 15390
 
1.0%
cambarus 10146
 
0.6%
cerithium 9393
 
0.6%
orconectes 8661
 
0.6%
procambarus 8047
 
0.5%
nassarius 6727
 
0.4%
lumbrineris 4967
 
0.3%
terebra 4662
 
0.3%
aricidea 4572
 
0.3%
Other values (21074) 1471629
93.8%
2025-01-07T10:46:18.625170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1744079
 
11.9%
i 1263792
 
8.7%
o 1156021
 
7.9%
e 1016938
 
7.0%
r 967987
 
6.6%
s 938349
 
6.4%
l 915577
 
6.3%
t 706525
 
4.8%
n 704068
 
4.8%
u 686498
 
4.7%
Other values (44) 4500179
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14600013
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1744079
 
11.9%
i 1263792
 
8.7%
o 1156021
 
7.9%
e 1016938
 
7.0%
r 967987
 
6.6%
s 938349
 
6.4%
l 915577
 
6.3%
t 706525
 
4.8%
n 704068
 
4.8%
u 686498
 
4.7%
Other values (44) 4500179
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14600013
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1744079
 
11.9%
i 1263792
 
8.7%
o 1156021
 
7.9%
e 1016938
 
7.0%
r 967987
 
6.6%
s 938349
 
6.4%
l 915577
 
6.3%
t 706525
 
4.8%
n 704068
 
4.8%
u 686498
 
4.7%
Other values (44) 4500179
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14600013
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1744079
 
11.9%
i 1263792
 
8.7%
o 1156021
 
7.9%
e 1016938
 
7.0%
r 967987
 
6.6%
s 938349
 
6.4%
l 915577
 
6.3%
t 706525
 
4.8%
n 704068
 
4.8%
u 686498
 
4.7%
Other values (44) 4500179
30.8%

subgenus
Boolean

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing1926391
Missing (%)> 99.9%
Memory size14.7 MiB
False
 
2
(Missing)
1926391 
ValueCountFrequency (%)
False 2
 
< 0.1%
(Missing) 1926391
> 99.9%
2025-01-07T10:46:18.694760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

infragenericEpithet
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean4493591.5
Minimum2504455
Maximum6482728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:18.734267image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2504455
5-th percentile2703368.65
Q13499023.25
median4493591.5
Q35488159.75
95-th percentile6283814.35
Maximum6482728
Range3978273
Interquartile range (IQR)1989136.5

Descriptive statistics

Standard deviation2813063.816
Coefficient of variation (CV)0.6260168099
Kurtosisnan
Mean4493591.5
Median Absolute Deviation (MAD)1989136.5
Skewnessnan
Sum8987183
Variance7.913328031 × 1012
MonotonicityStrictly decreasing
2025-01-07T10:46:18.784298image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
6482728 1
 
< 0.1%
2504455 1
 
< 0.1%
(Missing) 1926391
> 99.9%
ValueCountFrequency (%)
2504455 1
< 0.1%
6482728 1
< 0.1%
ValueCountFrequency (%)
6482728 1
< 0.1%
2504455 1
< 0.1%

specificEpithet
Text

Missing 

Distinct39412
Distinct (%)3.0%
Missing626798
Missing (%)32.5%
Memory size14.7 MiB
2025-01-07T10:46:18.990742image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length19
Mean length8.507768189
Min length2

Characters and Unicode

Total characters11056653
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9920 ?
Unique (%)0.8%

Sample

1st rowstriata
2nd rowcolumnaris
3rd rowsuensonii
4th rowlabrolineata
5th rowheteractis
ValueCountFrequency (%)
gracilis 6098
 
0.5%
fragilis 3477
 
0.3%
affinis 3341
 
0.3%
elegans 3182
 
0.2%
aculeata 3066
 
0.2%
borealis 2967
 
0.2%
americanus 2637
 
0.2%
grandis 2519
 
0.2%
acutus 2312
 
0.2%
tenuis 2265
 
0.2%
Other values (39402) 1267731
97.5%
2025-01-07T10:46:19.281628image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1553197
14.0%
i 1250540
11.3%
s 956883
 
8.7%
e 779958
 
7.1%
r 771552
 
7.0%
t 706671
 
6.4%
u 704699
 
6.4%
n 690520
 
6.2%
l 660182
 
6.0%
c 552656
 
5.0%
Other values (28) 2429795
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11056653
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1553197
14.0%
i 1250540
11.3%
s 956883
 
8.7%
e 779958
 
7.1%
r 771552
 
7.0%
t 706671
 
6.4%
u 704699
 
6.4%
n 690520
 
6.2%
l 660182
 
6.0%
c 552656
 
5.0%
Other values (28) 2429795
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11056653
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1553197
14.0%
i 1250540
11.3%
s 956883
 
8.7%
e 779958
 
7.1%
r 771552
 
7.0%
t 706671
 
6.4%
u 704699
 
6.4%
n 690520
 
6.2%
l 660182
 
6.0%
c 552656
 
5.0%
Other values (28) 2429795
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11056653
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1553197
14.0%
i 1250540
11.3%
s 956883
 
8.7%
e 779958
 
7.1%
r 771552
 
7.0%
t 706671
 
6.4%
u 704699
 
6.4%
n 690520
 
6.2%
l 660182
 
6.0%
c 552656
 
5.0%
Other values (28) 2429795
22.0%

infraspecificEpithet
Text

Missing 

Distinct3653
Distinct (%)10.1%
Missing1890289
Missing (%)98.1%
Memory size14.7 MiB
2025-01-07T10:46:19.503890image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length18
Median length16
Mean length8.605777753
Min length1

Characters and Unicode

Total characters310703
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1259 ?
Unique (%)3.5%

Sample

1st rowconnectens
2nd rowlaevis
3rd rowschizodontia
4th rowantarctica
5th rowsayi
ValueCountFrequency (%)
acutus 1011
 
2.8%
radiata 616
 
1.7%
bartonii 521
 
1.4%
gibbosus 501
 
1.4%
appressa 443
 
1.2%
campanulatum 379
 
1.0%
longimanus 359
 
1.0%
carinata 350
 
1.0%
floridana 283
 
0.8%
trivolvis 273
 
0.8%
Other values (3643) 31368
86.9%
2025-01-07T10:46:19.788798image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 45988
14.8%
i 33598
10.8%
s 29641
9.5%
e 22986
 
7.4%
n 22086
 
7.1%
u 19813
 
6.4%
r 19186
 
6.2%
t 17670
 
5.7%
l 16838
 
5.4%
c 16647
 
5.4%
Other values (19) 66250
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 310703
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 45988
14.8%
i 33598
10.8%
s 29641
9.5%
e 22986
 
7.4%
n 22086
 
7.1%
u 19813
 
6.4%
r 19186
 
6.2%
t 17670
 
5.7%
l 16838
 
5.4%
c 16647
 
5.4%
Other values (19) 66250
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 310703
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 45988
14.8%
i 33598
10.8%
s 29641
9.5%
e 22986
 
7.4%
n 22086
 
7.1%
u 19813
 
6.4%
r 19186
 
6.2%
t 17670
 
5.7%
l 16838
 
5.4%
c 16647
 
5.4%
Other values (19) 66250
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 310703
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 45988
14.8%
i 33598
10.8%
s 29641
9.5%
e 22986
 
7.4%
n 22086
 
7.1%
u 19813
 
6.4%
r 19186
 
6.2%
t 17670
 
5.7%
l 16838
 
5.4%
c 16647
 
5.4%
Other values (19) 66250
21.3%

cultivarEpithet
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing1926391
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean108
Minimum108
Maximum108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:19.855304image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum108
5-th percentile108
Q1108
median108
Q3108
95-th percentile108
Maximum108
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosisnan
Mean108
Median Absolute Deviation (MAD)0
Skewnessnan
Sum216
Variance0
MonotonicityIncreasing
2025-01-07T10:46:19.898357image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
108 2
 
< 0.1%
(Missing) 1926391
> 99.9%
ValueCountFrequency (%)
108 2
< 0.1%
ValueCountFrequency (%)
108 2
< 0.1%
Distinct14
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size14.7 MiB
2025-01-07T10:46:19.940867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length7
Mean length6.539588038
Min length3

Characters and Unicode

Total characters12597797
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowGENUS
2nd rowSPECIES
3rd rowSPECIES
4th rowSPECIES
5th rowSPECIES
ValueCountFrequency (%)
species 1263491
65.6%
genus 268755
 
14.0%
family 216656
 
11.2%
class 63569
 
3.3%
phylum 48164
 
2.5%
subspecies 32829
 
1.7%
order 26813
 
1.4%
kingdom 2836
 
0.1%
variety 2500
 
0.1%
form 773
 
< 0.1%
Other values (4) 4
 
< 0.1%
2025-01-07T10:46:20.055635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 3021362
24.0%
E 2890709
22.9%
I 1518312
12.1%
C 1359889
10.8%
P 1344484
10.7%
U 349749
 
2.8%
L 328389
 
2.6%
A 282726
 
2.2%
N 271593
 
2.2%
G 271591
 
2.2%
Other values (19) 958993
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12597797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 3021362
24.0%
E 2890709
22.9%
I 1518312
12.1%
C 1359889
10.8%
P 1344484
10.7%
U 349749
 
2.8%
L 328389
 
2.6%
A 282726
 
2.2%
N 271593
 
2.2%
G 271591
 
2.2%
Other values (19) 958993
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12597797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 3021362
24.0%
E 2890709
22.9%
I 1518312
12.1%
C 1359889
10.8%
P 1344484
10.7%
U 349749
 
2.8%
L 328389
 
2.6%
A 282726
 
2.2%
N 271593
 
2.2%
G 271591
 
2.2%
Other values (19) 958993
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12597797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 3021362
24.0%
E 2890709
22.9%
I 1518312
12.1%
C 1359889
10.8%
P 1344484
10.7%
U 349749
 
2.8%
L 328389
 
2.6%
A 282726
 
2.2%
N 271593
 
2.2%
G 271591
 
2.2%
Other values (19) 958993
 
7.6%

verbatimTaxonRank
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean662.5
Minimum434
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:20.111064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum434
5-th percentile456.85
Q1548.25
median662.5
Q3776.75
95-th percentile868.15
Maximum891
Range457
Interquartile range (IQR)228.5

Descriptive statistics

Standard deviation323.147799
Coefficient of variation (CV)0.4877702626
Kurtosisnan
Mean662.5
Median Absolute Deviation (MAD)228.5
Skewnessnan
Sum1325
Variance104424.5
MonotonicityStrictly decreasing
2025-01-07T10:46:20.154569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
891 1
 
< 0.1%
434 1
 
< 0.1%
(Missing) 1926391
> 99.9%
ValueCountFrequency (%)
434 1
< 0.1%
891 1
< 0.1%
ValueCountFrequency (%)
891 1
< 0.1%
434 1
< 0.1%

vernacularName
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean6190
Minimum5954
Maximum6426
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:20.195717image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5954
5-th percentile5977.6
Q16072
median6190
Q36308
95-th percentile6402.4
Maximum6426
Range472
Interquartile range (IQR)236

Descriptive statistics

Standard deviation333.7544007
Coefficient of variation (CV)0.05391831999
Kurtosisnan
Mean6190
Median Absolute Deviation (MAD)236
Skewnessnan
Sum12380
Variance111392
MonotonicityStrictly increasing
2025-01-07T10:46:20.240230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
5954 1
 
< 0.1%
6426 1
 
< 0.1%
(Missing) 1926391
> 99.9%
ValueCountFrequency (%)
5954 1
< 0.1%
6426 1
< 0.1%
ValueCountFrequency (%)
6426 1
< 0.1%
5954 1
< 0.1%

nomenclaturalCode
Unsupported

Missing  Rejected  Unsupported 

Missing1926389
Missing (%)> 99.9%
Memory size14.7 MiB
Distinct3
Distinct (%)< 0.1%
Missing2071
Missing (%)0.1%
Memory size14.7 MiB
2025-01-07T10:46:20.274230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.818195707
Min length7

Characters and Unicode

Total characters15044726
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSYNONYM
2nd rowACCEPTED
3rd rowACCEPTED
4th rowACCEPTED
5th rowSYNONYM
ValueCountFrequency (%)
accepted 1560511
81.1%
synonym 349850
 
18.2%
doubtful 13961
 
0.7%
2025-01-07T10:46:20.374165image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 3121022
20.7%
E 3121022
20.7%
T 1574472
10.5%
D 1574472
10.5%
A 1560511
10.4%
P 1560511
10.4%
Y 699700
 
4.7%
N 699700
 
4.7%
O 363811
 
2.4%
S 349850
 
2.3%
Other values (5) 419655
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15044726
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 3121022
20.7%
E 3121022
20.7%
T 1574472
10.5%
D 1574472
10.5%
A 1560511
10.4%
P 1560511
10.4%
Y 699700
 
4.7%
N 699700
 
4.7%
O 363811
 
2.4%
S 349850
 
2.3%
Other values (5) 419655
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15044726
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 3121022
20.7%
E 3121022
20.7%
T 1574472
10.5%
D 1574472
10.5%
A 1560511
10.4%
P 1560511
10.4%
Y 699700
 
4.7%
N 699700
 
4.7%
O 363811
 
2.4%
S 349850
 
2.3%
Other values (5) 419655
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15044726
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 3121022
20.7%
E 3121022
20.7%
T 1574472
10.5%
D 1574472
10.5%
A 1560511
10.4%
P 1560511
10.4%
Y 699700
 
4.7%
N 699700
 
4.7%
O 363811
 
2.4%
S 349850
 
2.3%
Other values (5) 419655
 
2.8%

nomenclaturalStatus
Real number (ℝ)

Missing 

Distinct2
Distinct (%)100.0%
Missing1926391
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean4493591.5
Minimum2504455
Maximum6482728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:20.428469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2504455
5-th percentile2703368.65
Q13499023.25
median4493591.5
Q35488159.75
95-th percentile6283814.35
Maximum6482728
Range3978273
Interquartile range (IQR)1989136.5

Descriptive statistics

Standard deviation2813063.816
Coefficient of variation (CV)0.6260168099
Kurtosisnan
Mean4493591.5
Median Absolute Deviation (MAD)1989136.5
Skewnessnan
Sum8987183
Variance7.913328031 × 1012
MonotonicityStrictly decreasing
2025-01-07T10:46:20.476469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
6482728 1
 
< 0.1%
2504455 1
 
< 0.1%
(Missing) 1926391
> 99.9%
ValueCountFrequency (%)
2504455 1
< 0.1%
6482728 1
< 0.1%
ValueCountFrequency (%)
6482728 1
< 0.1%
2504455 1
< 0.1%

taxonRemarks
Text

Missing 

Distinct3
Distinct (%)100.0%
Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:20.523617image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length19
Mean length16.33333333
Min length8

Characters and Unicode

Total characters49
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowHemionchos striatus
2nd rowNematoda
3rd rowConspicuum icteridorum
ValueCountFrequency (%)
hemionchos 1
20.0%
striatus 1
20.0%
nematoda 1
20.0%
conspicuum 1
20.0%
icteridorum 1
20.0%
2025-01-07T10:46:20.637173image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 5
10.2%
i 5
10.2%
u 4
 
8.2%
t 4
 
8.2%
m 4
 
8.2%
s 4
 
8.2%
a 3
 
6.1%
e 3
 
6.1%
c 3
 
6.1%
r 3
 
6.1%
Other values (8) 11
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 5
10.2%
i 5
10.2%
u 4
 
8.2%
t 4
 
8.2%
m 4
 
8.2%
s 4
 
8.2%
a 3
 
6.1%
e 3
 
6.1%
c 3
 
6.1%
r 3
 
6.1%
Other values (8) 11
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 5
10.2%
i 5
10.2%
u 4
 
8.2%
t 4
 
8.2%
m 4
 
8.2%
s 4
 
8.2%
a 3
 
6.1%
e 3
 
6.1%
c 3
 
6.1%
r 3
 
6.1%
Other values (8) 11
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 5
10.2%
i 5
10.2%
u 4
 
8.2%
t 4
 
8.2%
m 4
 
8.2%
s 4
 
8.2%
a 3
 
6.1%
e 3
 
6.1%
c 3
 
6.1%
r 3
 
6.1%
Other values (8) 11
22.4%
Distinct3
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size14.7 MiB
2025-01-07T10:46:20.711053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length46
Median length36
Mean length36.00000831
Min length36

Characters and Unicode

Total characters69350020
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row821cc27a-e3bb-4bc5-ac34-89ada245069d
2nd row821cc27a-e3bb-4bc5-ac34-89ada245069d
3rd row821cc27a-e3bb-4bc5-ac34-89ada245069d
4th row821cc27a-e3bb-4bc5-ac34-89ada245069d
5th row821cc27a-e3bb-4bc5-ac34-89ada245069d
ValueCountFrequency (%)
821cc27a-e3bb-4bc5-ac34-89ada245069d 1926387
> 99.9%
2
 
< 0.1%
striatus 1
 
< 0.1%
hemionchos 1
 
< 0.1%
campbell 1
 
< 0.1%
beveridge 1
 
< 0.1%
2006 1
 
< 0.1%
conspicuum 1
 
< 0.1%
icteridorum 1
 
< 0.1%
denton 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
2025-01-07T10:46:20.829269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 7705551
11.1%
a 7705550
11.1%
- 7705548
11.1%
2 5779162
8.3%
b 5779162
8.3%
4 5779161
8.3%
d 3852777
 
5.6%
9 3852775
 
5.6%
5 3852775
 
5.6%
8 3852774
 
5.6%
Other values (27) 13484785
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69350020
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 7705551
11.1%
a 7705550
11.1%
- 7705548
11.1%
2 5779162
8.3%
b 5779162
8.3%
4 5779161
8.3%
d 3852777
 
5.6%
9 3852775
 
5.6%
5 3852775
 
5.6%
8 3852774
 
5.6%
Other values (27) 13484785
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69350020
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 7705551
11.1%
a 7705550
11.1%
- 7705548
11.1%
2 5779162
8.3%
b 5779162
8.3%
4 5779161
8.3%
d 3852777
 
5.6%
9 3852775
 
5.6%
5 3852775
 
5.6%
8 3852774
 
5.6%
Other values (27) 13484785
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69350020
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 7705551
11.1%
a 7705550
11.1%
- 7705548
11.1%
2 5779162
8.3%
b 5779162
8.3%
4 5779161
8.3%
d 3852777
 
5.6%
9 3852775
 
5.6%
5 3852775
 
5.6%
8 3852774
 
5.6%
Other values (27) 13484785
19.4%
Distinct3
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size14.7 MiB
2025-01-07T10:46:20.873267image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length2
Mean length2.000019207
Min length2

Characters and Unicode

Total characters3852815
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 1926387
> 99.9%
hemionchos 1
 
< 0.1%
striatus 1
 
< 0.1%
conspicuum 1
 
< 0.1%
icteridorum 1
 
< 0.1%
2025-01-07T10:46:20.984010image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 1926387
50.0%
S 1926387
50.0%
i 5
 
< 0.1%
s 4
 
< 0.1%
o 4
 
< 0.1%
u 4
 
< 0.1%
c 3
 
< 0.1%
t 3
 
< 0.1%
r 3
 
< 0.1%
m 3
 
< 0.1%
Other values (9) 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3852815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 1926387
50.0%
S 1926387
50.0%
i 5
 
< 0.1%
s 4
 
< 0.1%
o 4
 
< 0.1%
u 4
 
< 0.1%
c 3
 
< 0.1%
t 3
 
< 0.1%
r 3
 
< 0.1%
m 3
 
< 0.1%
Other values (9) 12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3852815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 1926387
50.0%
S 1926387
50.0%
i 5
 
< 0.1%
s 4
 
< 0.1%
o 4
 
< 0.1%
u 4
 
< 0.1%
c 3
 
< 0.1%
t 3
 
< 0.1%
r 3
 
< 0.1%
m 3
 
< 0.1%
Other values (9) 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3852815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 1926387
50.0%
S 1926387
50.0%
i 5
 
< 0.1%
s 4
 
< 0.1%
o 4
 
< 0.1%
u 4
 
< 0.1%
c 3
 
< 0.1%
t 3
 
< 0.1%
r 3
 
< 0.1%
m 3
 
< 0.1%
Other values (9) 12
 
< 0.1%
Distinct209948
Distinct (%)10.9%
Missing6
Missing (%)< 0.1%
Memory size14.7 MiB
2025-01-07T10:46:21.147825image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length23.99591152
Min length20

Characters and Unicode

Total characters46225412
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9123 ?
Unique (%)0.5%

Sample

1st row2024-12-02T13:57:44.311Z
2nd row2024-12-02T13:57:20.485Z
3rd row2024-12-02T13:57:18.447Z
4th row2024-12-02T13:57:45.124Z
5th row2024-12-02T13:57:20.489Z
ValueCountFrequency (%)
2024-12-02t13:57:28.783z 37
 
< 0.1%
2024-12-02t13:57:52.889z 37
 
< 0.1%
2024-12-02t13:57:43.700z 36
 
< 0.1%
2024-12-02t13:57:40.815z 36
 
< 0.1%
2024-12-02t13:58:01.714z 36
 
< 0.1%
2024-12-02t13:57:50.671z 35
 
< 0.1%
2024-12-02t13:57:53.093z 35
 
< 0.1%
2024-12-02t13:57:40.927z 35
 
< 0.1%
2024-12-02t13:57:28.440z 35
 
< 0.1%
2024-12-02t13:57:33.269z 35
 
< 0.1%
Other values (209938) 1926030
> 99.9%
2025-01-07T10:46:21.382202image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 8796773
19.0%
0 4884695
10.6%
1 4858658
10.5%
- 3852774
8.3%
: 3852774
8.3%
4 3098095
 
6.7%
5 3058765
 
6.6%
3 3051121
 
6.6%
T 1926387
 
4.2%
Z 1926387
 
4.2%
Other values (5) 6918983
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46225412
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 8796773
19.0%
0 4884695
10.6%
1 4858658
10.5%
- 3852774
8.3%
: 3852774
8.3%
4 3098095
 
6.7%
5 3058765
 
6.6%
3 3051121
 
6.6%
T 1926387
 
4.2%
Z 1926387
 
4.2%
Other values (5) 6918983
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46225412
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 8796773
19.0%
0 4884695
10.6%
1 4858658
10.5%
- 3852774
8.3%
: 3852774
8.3%
4 3098095
 
6.7%
5 3058765
 
6.6%
3 3051121
 
6.6%
T 1926387
 
4.2%
Z 1926387
 
4.2%
Other values (5) 6918983
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46225412
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 8796773
19.0%
0 4884695
10.6%
1 4858658
10.5%
- 3852774
8.3%
: 3852774
8.3%
4 3098095
 
6.7%
5 3058765
 
6.6%
3 3051121
 
6.6%
T 1926387
 
4.2%
Z 1926387
 
4.2%
Other values (5) 6918983
15.0%

elevation
Unsupported

Missing  Rejected  Unsupported 

Missing1919570
Missing (%)99.6%
Memory size14.7 MiB

elevationAccuracy
Unsupported

Missing  Rejected  Unsupported 

Missing1922885
Missing (%)99.8%
Memory size14.7 MiB

depth
Unsupported

Missing  Rejected  Unsupported 

Missing1143682
Missing (%)59.4%
Memory size14.7 MiB

depthAccuracy
Unsupported

Missing  Rejected  Unsupported 

Missing1205339
Missing (%)62.6%
Memory size14.7 MiB

distanceFromCentroidInMeters
Unsupported

Missing  Rejected  Unsupported 

Missing1917545
Missing (%)99.5%
Memory size14.7 MiB

issue
Text

Distinct402
Distinct (%)< 0.1%
Missing37
Missing (%)< 0.1%
Memory size14.7 MiB
2025-01-07T10:46:21.463761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length209
Median length204
Mean length89.0387161
Min length8

Characters and Unicode

Total characters171520265
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)< 0.1%

Sample

1st rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT;GEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_INVALID
2nd rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
3rd rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT;CONTINENT_DERIVED_FROM_COUNTRY;CONTINENT_INVALID
4th rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT;GEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_INVALID
5th rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT;CONTINENT_DERIVED_FROM_COUNTRY
ValueCountFrequency (%)
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;continent_invalid 516478
26.8%
occurrence_status_inferred_from_individual_count 418366
21.7%
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;continent_derived_from_coordinates;continent_invalid 224592
11.7%
occurrence_status_inferred_from_individual_count;continent_derived_from_country;continent_invalid 212163
11.0%
occurrence_status_inferred_from_individual_count;continent_derived_from_country 195778
 
10.2%
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;continent_derived_from_coordinates 50454
 
2.6%
occurrence_status_inferred_from_individual_count;taxon_match_higherrank 36575
 
1.9%
occurrence_status_inferred_from_individual_count;continent_derived_from_coordinates 32128
 
1.7%
occurrence_status_inferred_from_individual_count;country_derived_from_coordinates;geodetic_datum_assumed_wgs84;continent_invalid 27721
 
1.4%
occurrence_status_inferred_from_individual_count;continent_derived_from_country;taxon_match_higherrank 25845
 
1.3%
Other values (392) 186256
 
9.7%
2025-01-07T10:46:21.721294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 16496084
9.6%
N 15722593
 
9.2%
E 14675877
 
8.6%
I 14123173
 
8.2%
T 12772362
 
7.4%
R 12496875
 
7.3%
D 11817130
 
6.9%
C 11680988
 
6.8%
O 10988569
 
6.4%
U 10155291
 
5.9%
Other values (24) 40591323
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 171520265
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 16496084
9.6%
N 15722593
 
9.2%
E 14675877
 
8.6%
I 14123173
 
8.2%
T 12772362
 
7.4%
R 12496875
 
7.3%
D 11817130
 
6.9%
C 11680988
 
6.8%
O 10988569
 
6.4%
U 10155291
 
5.9%
Other values (24) 40591323
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 171520265
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 16496084
9.6%
N 15722593
 
9.2%
E 14675877
 
8.6%
I 14123173
 
8.2%
T 12772362
 
7.4%
R 12496875
 
7.3%
D 11817130
 
6.9%
C 11680988
 
6.8%
O 10988569
 
6.4%
U 10155291
 
5.9%
Other values (24) 40591323
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 171520265
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 16496084
9.6%
N 15722593
 
9.2%
E 14675877
 
8.6%
I 14123173
 
8.2%
T 12772362
 
7.4%
R 12496875
 
7.3%
D 11817130
 
6.9%
C 11680988
 
6.8%
O 10988569
 
6.4%
U 10155291
 
5.9%
Other values (24) 40591323
23.7%

mediaType
Text

Missing 

Distinct73
Distinct (%)< 0.1%
Missing1683241
Missing (%)87.4%
Memory size14.7 MiB
2025-01-07T10:46:21.793827image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length1704
Median length10
Mean length13.26034744
Min length5

Characters and Unicode

Total characters3224280
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowStillImage
2nd rowStillImage
3rd rowStillImage
4th rowStillImage
5th rowStillImage
ValueCountFrequency (%)
stillimage 220054
90.5%
stillimage;stillimage 12696
 
5.2%
stillimage;stillimage;stillimage 3561
 
1.5%
stillimage;stillimage;stillimage;stillimage 2030
 
0.8%
stillimage;stillimage;stillimage;stillimage;stillimage 1055
 
0.4%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 769
 
0.3%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 533
 
0.2%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 390
 
0.2%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 309
 
0.1%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 213
 
0.1%
Other values (63) 1542
 
0.6%
2025-01-07T10:46:21.952949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 630442
19.6%
a 315222
9.8%
e 315222
9.8%
S 315220
9.8%
i 315220
9.8%
t 315220
9.8%
m 315220
9.8%
I 315220
9.8%
g 315220
9.8%
; 72070
 
2.2%
Other values (2) 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3224280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 630442
19.6%
a 315222
9.8%
e 315222
9.8%
S 315220
9.8%
i 315220
9.8%
t 315220
9.8%
m 315220
9.8%
I 315220
9.8%
g 315220
9.8%
; 72070
 
2.2%
Other values (2) 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3224280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 630442
19.6%
a 315222
9.8%
e 315222
9.8%
S 315220
9.8%
i 315220
9.8%
t 315220
9.8%
m 315220
9.8%
I 315220
9.8%
g 315220
9.8%
; 72070
 
2.2%
Other values (2) 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3224280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 630442
19.6%
a 315222
9.8%
e 315222
9.8%
S 315220
9.8%
i 315220
9.8%
t 315220
9.8%
m 315220
9.8%
I 315220
9.8%
g 315220
9.8%
; 72070
 
2.2%
Other values (2) 4
 
< 0.1%

hasCoordinate
Unsupported

Rejected  Unsupported 

Missing1
Missing (%)< 0.1%
Memory size14.7 MiB

hasGeospatialIssues
Unsupported

Rejected  Unsupported 

Missing4
Missing (%)< 0.1%
Memory size14.7 MiB

taxonKey
Unsupported

Rejected  Unsupported 

Missing5
Missing (%)< 0.1%
Memory size14.7 MiB

acceptedTaxonKey
Unsupported

Rejected  Unsupported 

Missing2070
Missing (%)0.1%
Memory size14.7 MiB

kingdomKey
Unsupported

Rejected  Unsupported 

Missing5
Missing (%)< 0.1%
Memory size14.7 MiB

phylumKey
Unsupported

Rejected  Unsupported 

Missing3161
Missing (%)0.2%
Memory size14.7 MiB

classKey
Unsupported

Missing  Rejected  Unsupported 

Missing66158
Missing (%)3.4%
Memory size14.7 MiB

orderKey
Unsupported

Missing  Rejected  Unsupported 

Missing329533
Missing (%)17.1%
Memory size14.7 MiB

familyKey
Unsupported

Missing  Rejected  Unsupported 

Missing144485
Missing (%)7.5%
Memory size14.7 MiB

genusKey
Unsupported

Missing  Rejected  Unsupported 

Missing358041
Missing (%)18.6%
Memory size14.7 MiB

subgenusKey
Text

Missing 

Distinct4
Distinct (%)80.0%
Missing1926388
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:22.009379image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length11
Mean length8.6
Min length2

Characters and Unicode

Total characters43
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)60.0%

Sample

1st rowNE
2nd rowPalaeacanthocephala
3rd rowChromadorea
4th rowMonogenea
5th rowNE
ValueCountFrequency (%)
ne 2
40.0%
palaeacanthocephala 1
20.0%
chromadorea 1
20.0%
monogenea 1
20.0%
2025-01-07T10:46:22.112480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 9
20.9%
e 5
11.6%
o 5
11.6%
h 3
 
7.0%
n 3
 
7.0%
N 2
 
4.7%
E 2
 
4.7%
c 2
 
4.7%
r 2
 
4.7%
l 2
 
4.7%
Other values (8) 8
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 9
20.9%
e 5
11.6%
o 5
11.6%
h 3
 
7.0%
n 3
 
7.0%
N 2
 
4.7%
E 2
 
4.7%
c 2
 
4.7%
r 2
 
4.7%
l 2
 
4.7%
Other values (8) 8
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 9
20.9%
e 5
11.6%
o 5
11.6%
h 3
 
7.0%
n 3
 
7.0%
N 2
 
4.7%
E 2
 
4.7%
c 2
 
4.7%
r 2
 
4.7%
l 2
 
4.7%
Other values (8) 8
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 9
20.9%
e 5
11.6%
o 5
11.6%
h 3
 
7.0%
n 3
 
7.0%
N 2
 
4.7%
E 2
 
4.7%
c 2
 
4.7%
r 2
 
4.7%
l 2
 
4.7%
Other values (8) 8
18.6%

speciesKey
Unsupported

Missing  Rejected  Unsupported 

Missing626819
Missing (%)32.5%
Memory size14.7 MiB

species
Text

Missing 

Distinct81449
Distinct (%)6.3%
Missing626822
Missing (%)32.5%
Memory size14.7 MiB
2025-01-07T10:46:22.326725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length41
Median length36
Mean length18.98173243
Min length7

Characters and Unicode

Total characters24668109
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23438 ?
Unique (%)1.8%

Sample

1st rowBulla striata
2nd rowStylopathes columnaris
3rd rowOphiothrix suensonii
4th rowNaria labrolineata
5th rowLysasterias heteractis
ValueCountFrequency (%)
conus 21648
 
0.8%
cerithium 8891
 
0.3%
cambarus 8740
 
0.3%
faxonius 8187
 
0.3%
procambarus 8031
 
0.3%
gracilis 6079
 
0.2%
aricidea 4891
 
0.2%
nassarius 4086
 
0.2%
pagurus 3943
 
0.2%
oliva 3823
 
0.1%
Other values (55326) 2520823
97.0%
2025-01-07T10:46:22.626547image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3034205
12.3%
i 2321115
 
9.4%
s 1756467
 
7.1%
e 1634157
 
6.6%
r 1566246
 
6.3%
o 1518962
 
6.2%
l 1442638
 
5.8%
t 1302012
 
5.3%
1299571
 
5.3%
u 1297622
 
5.3%
Other values (44) 7495114
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24668109
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3034205
12.3%
i 2321115
 
9.4%
s 1756467
 
7.1%
e 1634157
 
6.6%
r 1566246
 
6.3%
o 1518962
 
6.2%
l 1442638
 
5.8%
t 1302012
 
5.3%
1299571
 
5.3%
u 1297622
 
5.3%
Other values (44) 7495114
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24668109
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3034205
12.3%
i 2321115
 
9.4%
s 1756467
 
7.1%
e 1634157
 
6.6%
r 1566246
 
6.3%
o 1518962
 
6.2%
l 1442638
 
5.8%
t 1302012
 
5.3%
1299571
 
5.3%
u 1297622
 
5.3%
Other values (44) 7495114
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24668109
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3034205
12.3%
i 2321115
 
9.4%
s 1756467
 
7.1%
e 1634157
 
6.6%
r 1566246
 
6.3%
o 1518962
 
6.2%
l 1442638
 
5.8%
t 1302012
 
5.3%
1299571
 
5.3%
u 1297622
 
5.3%
Other values (44) 7495114
30.4%
Distinct94525
Distinct (%)4.9%
Missing2067
Missing (%)0.1%
Memory size14.7 MiB
2025-01-07T10:46:22.857025image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length188
Median length120
Mean length29.47398518
Min length6

Characters and Unicode

Total characters56717556
Distinct characters115
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27025 ?
Unique (%)1.4%

Sample

1st rowSycon Risso, 1827
2nd rowBulla striata Bruguière, 1792
3rd rowStylopathes columnaris (Duchassaing, 1870)
4th rowOphiothrix suensonii Lütken, 1856
5th rowNaria labrolineata (Gaskoin, 1849)
ValueCountFrequency (%)
137132
 
2.0%
linnaeus 102227
 
1.5%
1758 86436
 
1.3%
say 52030
 
0.8%
lamarck 41218
 
0.6%
dall 26280
 
0.4%
1791 25378
 
0.4%
gmelin 24581
 
0.4%
gastropoda 23786
 
0.4%
conus 22951
 
0.3%
Other values (67808) 6231685
92.0%
2025-01-07T10:46:23.173461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4975492
 
8.8%
4849378
 
8.6%
i 3756798
 
6.6%
e 3416706
 
6.0%
r 2838443
 
5.0%
s 2680069
 
4.7%
o 2507220
 
4.4%
l 2493288
 
4.4%
n 2462018
 
4.3%
t 1946947
 
3.4%
Other values (105) 24791197
43.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56717556
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4975492
 
8.8%
4849378
 
8.6%
i 3756798
 
6.6%
e 3416706
 
6.0%
r 2838443
 
5.0%
s 2680069
 
4.7%
o 2507220
 
4.4%
l 2493288
 
4.4%
n 2462018
 
4.3%
t 1946947
 
3.4%
Other values (105) 24791197
43.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56717556
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4975492
 
8.8%
4849378
 
8.6%
i 3756798
 
6.6%
e 3416706
 
6.0%
r 2838443
 
5.0%
s 2680069
 
4.7%
o 2507220
 
4.4%
l 2493288
 
4.4%
n 2462018
 
4.3%
t 1946947
 
3.4%
Other values (105) 24791197
43.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56717556
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4975492
 
8.8%
4849378
 
8.6%
i 3756798
 
6.6%
e 3416706
 
6.0%
r 2838443
 
5.0%
s 2680069
 
4.7%
o 2507220
 
4.4%
l 2493288
 
4.4%
n 2462018
 
4.3%
t 1946947
 
3.4%
Other values (105) 24791197
43.7%
Distinct133993
Distinct (%)8.5%
Missing353775
Missing (%)18.4%
Memory size14.7 MiB
2025-01-07T10:46:23.408469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length85
Median length59
Mean length19.44688666
Min length4

Characters and Unicode

Total characters30582524
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51619 ?
Unique (%)3.3%

Sample

1st rowScypha sp.
2nd rowBulla striata
3rd rowStylopathes columnaris
4th rowOphiothrix suensonii
5th rowCypraea labrolineata
ValueCountFrequency (%)
sp 198063
 
6.0%
conus 24328
 
0.7%
cypraea 15395
 
0.5%
cambarus 12003
 
0.4%
cerithium 9397
 
0.3%
orconectes 8683
 
0.3%
procambarus 8141
 
0.2%
nassarius 6728
 
0.2%
gracilis 6632
 
0.2%
terebra 5168
 
0.2%
Other values (70829) 3025211
91.1%
2025-01-07T10:46:23.719865image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3610431
 
11.8%
i 2750408
 
9.0%
s 2277504
 
7.4%
e 1954344
 
6.4%
r 1901340
 
6.2%
o 1840596
 
6.0%
1747131
 
5.7%
l 1714269
 
5.6%
n 1541700
 
5.0%
t 1537214
 
5.0%
Other values (68) 9707587
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30582524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3610431
 
11.8%
i 2750408
 
9.0%
s 2277504
 
7.4%
e 1954344
 
6.4%
r 1901340
 
6.2%
o 1840596
 
6.0%
1747131
 
5.7%
l 1714269
 
5.6%
n 1541700
 
5.0%
t 1537214
 
5.0%
Other values (68) 9707587
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30582524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3610431
 
11.8%
i 2750408
 
9.0%
s 2277504
 
7.4%
e 1954344
 
6.4%
r 1901340
 
6.2%
o 1840596
 
6.0%
1747131
 
5.7%
l 1714269
 
5.6%
n 1541700
 
5.0%
t 1537214
 
5.0%
Other values (68) 9707587
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30582524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3610431
 
11.8%
i 2750408
 
9.0%
s 2277504
 
7.4%
e 1954344
 
6.4%
r 1901340
 
6.2%
o 1840596
 
6.0%
1747131
 
5.7%
l 1714269
 
5.6%
n 1541700
 
5.0%
t 1537214
 
5.0%
Other values (68) 9707587
31.7%

typifiedName
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

protocol
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing6
Missing (%)< 0.1%
Memory size14.7 MiB
2025-01-07T10:46:23.777868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5779161
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEML
2nd rowEML
3rd rowEML
4th rowEML
5th rowEML
ValueCountFrequency (%)
eml 1926387
100.0%
2025-01-07T10:46:23.869282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1926387
33.3%
M 1926387
33.3%
L 1926387
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5779161
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1926387
33.3%
M 1926387
33.3%
L 1926387
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5779161
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1926387
33.3%
M 1926387
33.3%
L 1926387
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5779161
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1926387
33.3%
M 1926387
33.3%
L 1926387
33.3%
Distinct209952
Distinct (%)10.9%
Missing2
Missing (%)< 0.1%
Memory size14.7 MiB
2025-01-07T10:46:24.029365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length23.99588194
Min length7

Characters and Unicode

Total characters46225451
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9127 ?
Unique (%)0.5%

Sample

1st row2024-12-02T13:57:44.311Z
2nd row2024-12-02T13:57:20.485Z
3rd row2024-12-02T13:57:18.447Z
4th row2024-12-02T13:57:45.124Z
5th row2024-12-02T13:57:20.489Z
ValueCountFrequency (%)
2024-12-02t13:57:52.889z 37
 
< 0.1%
2024-12-02t13:57:28.783z 37
 
< 0.1%
2024-12-02t13:57:43.700z 36
 
< 0.1%
2024-12-02t13:58:01.714z 36
 
< 0.1%
2024-12-02t13:57:40.815z 36
 
< 0.1%
2024-12-02t13:57:29.347z 35
 
< 0.1%
2024-12-02t13:57:30.406z 35
 
< 0.1%
2024-12-02t13:57:41.994z 35
 
< 0.1%
2024-12-02t13:57:33.269z 35
 
< 0.1%
2024-12-02t13:57:50.671z 35
 
< 0.1%
Other values (209942) 1926034
> 99.9%
2025-01-07T10:46:24.265367image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 8796773
19.0%
0 4884695
10.6%
1 4858658
10.5%
- 3852774
8.3%
: 3852774
8.3%
4 3098095
 
6.7%
5 3058765
 
6.6%
3 3051121
 
6.6%
T 1926388
 
4.2%
Z 1926387
 
4.2%
Other values (24) 6919021
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46225451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 8796773
19.0%
0 4884695
10.6%
1 4858658
10.5%
- 3852774
8.3%
: 3852774
8.3%
4 3098095
 
6.7%
5 3058765
 
6.6%
3 3051121
 
6.6%
T 1926388
 
4.2%
Z 1926387
 
4.2%
Other values (24) 6919021
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46225451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 8796773
19.0%
0 4884695
10.6%
1 4858658
10.5%
- 3852774
8.3%
: 3852774
8.3%
4 3098095
 
6.7%
5 3058765
 
6.6%
3 3051121
 
6.6%
T 1926388
 
4.2%
Z 1926387
 
4.2%
Other values (24) 6919021
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46225451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 8796773
19.0%
0 4884695
10.6%
1 4858658
10.5%
- 3852774
8.3%
: 3852774
8.3%
4 3098095
 
6.7%
5 3058765
 
6.6%
3 3051121
 
6.6%
T 1926388
 
4.2%
Z 1926387
 
4.2%
Other values (24) 6919021
15.0%
Distinct4
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size14.7 MiB
2025-01-07T10:46:24.332303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length23.99997872
Min length7

Characters and Unicode

Total characters46233319
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row2024-12-02T11:48:23.416Z
2nd row2024-12-02T11:48:23.416Z
3rd row2024-12-02T11:48:23.416Z
4th row2024-12-02T11:48:23.416Z
5th row2024-12-02T11:48:23.416Z
ValueCountFrequency (%)
2024-12-02t11:48:23.416z 1926387
> 99.9%
echinorhynchus 1
 
< 0.1%
setaria 1
 
< 0.1%
sphyranura 1
 
< 0.1%
2025-01-07T10:46:24.441495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 9631935
20.8%
1 7705548
16.7%
4 5779161
12.5%
0 3852774
 
8.3%
- 3852774
 
8.3%
: 3852774
 
8.3%
T 1926387
 
4.2%
8 1926387
 
4.2%
3 1926387
 
4.2%
. 1926387
 
4.2%
Other values (17) 3852805
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46233319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 9631935
20.8%
1 7705548
16.7%
4 5779161
12.5%
0 3852774
 
8.3%
- 3852774
 
8.3%
: 3852774
 
8.3%
T 1926387
 
4.2%
8 1926387
 
4.2%
3 1926387
 
4.2%
. 1926387
 
4.2%
Other values (17) 3852805
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46233319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 9631935
20.8%
1 7705548
16.7%
4 5779161
12.5%
0 3852774
 
8.3%
- 3852774
 
8.3%
: 3852774
 
8.3%
T 1926387
 
4.2%
8 1926387
 
4.2%
3 1926387
 
4.2%
. 1926387
 
4.2%
Other values (17) 3852805
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46233319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 9631935
20.8%
1 7705548
16.7%
4 5779161
12.5%
0 3852774
 
8.3%
- 3852774
 
8.3%
: 3852774
 
8.3%
T 1926387
 
4.2%
8 1926387
 
4.2%
3 1926387
 
4.2%
. 1926387
 
4.2%
Other values (17) 3852805
8.3%

repatriated
Boolean

Missing 

Distinct2
Distinct (%)< 0.1%
Missing110144
Missing (%)5.7%
Memory size14.7 MiB
True
947669 
False
868580 
(Missing)
110144 
ValueCountFrequency (%)
True 947669
49.2%
False 868580
45.1%
(Missing) 110144
 
5.7%
2025-01-07T10:46:24.496002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

relativeOrganismQuantity
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing1926392
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:24.541515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters36
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row821cc27a-e3bb-4bc5-ac34-89ada245069d
ValueCountFrequency (%)
821cc27a-e3bb-4bc5-ac34-89ada245069d 1
100.0%
2025-01-07T10:46:24.642690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 4
11.1%
a 4
11.1%
- 4
11.1%
2 3
8.3%
4 3
8.3%
b 3
8.3%
8 2
 
5.6%
3 2
 
5.6%
9 2
 
5.6%
d 2
 
5.6%
Other values (6) 7
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 4
11.1%
a 4
11.1%
- 4
11.1%
2 3
8.3%
4 3
8.3%
b 3
8.3%
8 2
 
5.6%
3 2
 
5.6%
9 2
 
5.6%
d 2
 
5.6%
Other values (6) 7
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 4
11.1%
a 4
11.1%
- 4
11.1%
2 3
8.3%
4 3
8.3%
b 3
8.3%
8 2
 
5.6%
3 2
 
5.6%
9 2
 
5.6%
d 2
 
5.6%
Other values (6) 7
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 4
11.1%
a 4
11.1%
- 4
11.1%
2 3
8.3%
4 3
8.3%
b 3
8.3%
8 2
 
5.6%
3 2
 
5.6%
9 2
 
5.6%
d 2
 
5.6%
Other values (6) 7
19.4%

projectId
Text

Missing 

Distinct3
Distinct (%)100.0%
Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:24.698171image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length12
Mean length10
Min length2

Characters and Unicode

Total characters30
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowlageniformis
2nd rowUS
3rd rowlabiatopapillosa
ValueCountFrequency (%)
lageniformis 1
33.3%
us 1
33.3%
labiatopapillosa 1
33.3%
2025-01-07T10:46:24.807168image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5
16.7%
l 4
13.3%
i 4
13.3%
o 3
10.0%
p 2
 
6.7%
s 2
 
6.7%
e 1
 
3.3%
g 1
 
3.3%
f 1
 
3.3%
n 1
 
3.3%
Other values (6) 6
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5
16.7%
l 4
13.3%
i 4
13.3%
o 3
10.0%
p 2
 
6.7%
s 2
 
6.7%
e 1
 
3.3%
g 1
 
3.3%
f 1
 
3.3%
n 1
 
3.3%
Other values (6) 6
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5
16.7%
l 4
13.3%
i 4
13.3%
o 3
10.0%
p 2
 
6.7%
s 2
 
6.7%
e 1
 
3.3%
g 1
 
3.3%
f 1
 
3.3%
n 1
 
3.3%
Other values (6) 6
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5
16.7%
l 4
13.3%
i 4
13.3%
o 3
10.0%
p 2
 
6.7%
s 2
 
6.7%
e 1
 
3.3%
g 1
 
3.3%
f 1
 
3.3%
n 1
 
3.3%
Other values (6) 6
20.0%

isSequenced
Unsupported

Rejected  Unsupported 

Missing5
Missing (%)< 0.1%
Memory size14.7 MiB

gbifRegion
Text

Missing 

Distinct7
Distinct (%)< 0.1%
Missing115678
Missing (%)6.0%
Memory size14.7 MiB
2025-01-07T10:46:24.859672image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length10.88896817
Min length4

Characters and Unicode

Total characters19716818
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTH_AMERICA
2nd rowNORTH_AMERICA
3rd rowLATIN_AMERICA
4th rowNORTH_AMERICA
5th rowASIA
ValueCountFrequency (%)
north_america 900416
49.7%
latin_america 368762
20.4%
asia 206888
 
11.4%
oceania 167374
 
9.2%
africa 56930
 
3.1%
europe 56674
 
3.1%
antarctica 53671
 
3.0%
2025-01-07T10:46:24.968195image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 3930515
19.9%
R 2336869
11.9%
I 2122803
10.8%
C 1600824
8.1%
E 1549900
 
7.9%
N 1490223
 
7.6%
T 1376520
 
7.0%
M 1269178
 
6.4%
_ 1269178
 
6.4%
O 1124464
 
5.7%
Other values (6) 1646344
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19716818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3930515
19.9%
R 2336869
11.9%
I 2122803
10.8%
C 1600824
8.1%
E 1549900
 
7.9%
N 1490223
 
7.6%
T 1376520
 
7.0%
M 1269178
 
6.4%
_ 1269178
 
6.4%
O 1124464
 
5.7%
Other values (6) 1646344
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19716818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3930515
19.9%
R 2336869
11.9%
I 2122803
10.8%
C 1600824
8.1%
E 1549900
 
7.9%
N 1490223
 
7.6%
T 1376520
 
7.0%
M 1269178
 
6.4%
_ 1269178
 
6.4%
O 1124464
 
5.7%
Other values (6) 1646344
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19716818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3930515
19.9%
R 2336869
11.9%
I 2122803
10.8%
C 1600824
8.1%
E 1549900
 
7.9%
N 1490223
 
7.6%
T 1376520
 
7.0%
M 1269178
 
6.4%
_ 1269178
 
6.4%
O 1124464
 
5.7%
Other values (6) 1646344
8.3%
Distinct3
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size14.7 MiB
2025-01-07T10:46:25.017981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.99998962
Min length5

Characters and Unicode

Total characters25043050
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNORTH_AMERICA
2nd rowNORTH_AMERICA
3rd rowNORTH_AMERICA
4th rowNORTH_AMERICA
5th rowNORTH_AMERICA
ValueCountFrequency (%)
north_america 1926387
> 99.9%
species 2
 
< 0.1%
genus 1
 
< 0.1%
2025-01-07T10:46:25.128083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 3852774
15.4%
A 3852774
15.4%
E 1926392
7.7%
I 1926389
7.7%
C 1926389
7.7%
N 1926388
7.7%
O 1926387
7.7%
_ 1926387
7.7%
H 1926387
7.7%
T 1926387
7.7%
Other values (5) 1926396
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25043050
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 3852774
15.4%
A 3852774
15.4%
E 1926392
7.7%
I 1926389
7.7%
C 1926389
7.7%
N 1926388
7.7%
O 1926387
7.7%
_ 1926387
7.7%
H 1926387
7.7%
T 1926387
7.7%
Other values (5) 1926396
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25043050
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 3852774
15.4%
A 3852774
15.4%
E 1926392
7.7%
I 1926389
7.7%
C 1926389
7.7%
N 1926388
7.7%
O 1926387
7.7%
_ 1926387
7.7%
H 1926387
7.7%
T 1926387
7.7%
Other values (5) 1926396
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25043050
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 3852774
15.4%
A 3852774
15.4%
E 1926392
7.7%
I 1926389
7.7%
C 1926389
7.7%
N 1926388
7.7%
O 1926387
7.7%
_ 1926387
7.7%
H 1926387
7.7%
T 1926387
7.7%
Other values (5) 1926396
7.7%

level0Gid
Text

Missing 

Distinct226
Distinct (%)0.1%
Missing1691070
Missing (%)87.8%
Memory size14.7 MiB
2025-01-07T10:46:25.305617image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters705969
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st rowUSA
2nd rowPAN
3rd rowUSA
4th rowUSA
5th rowPAN
ValueCountFrequency (%)
usa 138756
59.0%
pan 11701
 
5.0%
jpn 8794
 
3.7%
mex 4690
 
2.0%
phl 4467
 
1.9%
can 4382
 
1.9%
dom 3446
 
1.5%
cri 3146
 
1.3%
mdg 2984
 
1.3%
pri 2846
 
1.2%
Other values (216) 50111
 
21.3%
2025-01-07T10:46:25.554054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 169536
24.0%
U 149988
21.2%
S 147144
20.8%
N 36249
 
5.1%
P 32498
 
4.6%
M 17314
 
2.5%
C 16563
 
2.3%
R 16511
 
2.3%
I 11617
 
1.6%
J 11408
 
1.6%
Other values (18) 97141
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 705969
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 169536
24.0%
U 149988
21.2%
S 147144
20.8%
N 36249
 
5.1%
P 32498
 
4.6%
M 17314
 
2.5%
C 16563
 
2.3%
R 16511
 
2.3%
I 11617
 
1.6%
J 11408
 
1.6%
Other values (18) 97141
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 705969
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 169536
24.0%
U 149988
21.2%
S 147144
20.8%
N 36249
 
5.1%
P 32498
 
4.6%
M 17314
 
2.5%
C 16563
 
2.3%
R 16511
 
2.3%
I 11617
 
1.6%
J 11408
 
1.6%
Other values (18) 97141
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 705969
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 169536
24.0%
U 149988
21.2%
S 147144
20.8%
N 36249
 
5.1%
P 32498
 
4.6%
M 17314
 
2.5%
C 16563
 
2.3%
R 16511
 
2.3%
I 11617
 
1.6%
J 11408
 
1.6%
Other values (18) 97141
13.8%

level0Name
Text

Missing 

Distinct226
Distinct (%)0.1%
Missing1691070
Missing (%)87.8%
Memory size14.7 MiB
2025-01-07T10:46:25.756752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length13
Mean length11.1625043
Min length4

Characters and Unicode

Total characters2626794
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st rowUnited States
2nd rowPanama
3rd rowUnited States
4th rowUnited States
5th rowPanama
ValueCountFrequency (%)
united 139310
34.8%
states 138840
34.7%
panama 11701
 
2.9%
japan 8794
 
2.2%
méxico 4690
 
1.2%
philippines 4467
 
1.1%
canada 4382
 
1.1%
republic 3662
 
0.9%
dominican 3446
 
0.9%
rica 3146
 
0.8%
Other values (265) 77445
19.4%
2025-01-07T10:46:26.028338image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 437054
16.6%
e 318254
12.1%
a 296155
11.3%
i 217274
8.3%
n 208865
8.0%
s 170178
 
6.5%
164560
 
6.3%
d 159630
 
6.1%
S 144075
 
5.5%
U 140430
 
5.3%
Other values (52) 370319
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2626794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 437054
16.6%
e 318254
12.1%
a 296155
11.3%
i 217274
8.3%
n 208865
8.0%
s 170178
 
6.5%
164560
 
6.3%
d 159630
 
6.1%
S 144075
 
5.5%
U 140430
 
5.3%
Other values (52) 370319
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2626794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 437054
16.6%
e 318254
12.1%
a 296155
11.3%
i 217274
8.3%
n 208865
8.0%
s 170178
 
6.5%
164560
 
6.3%
d 159630
 
6.1%
S 144075
 
5.5%
U 140430
 
5.3%
Other values (52) 370319
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2626794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 437054
16.6%
e 318254
12.1%
a 296155
11.3%
i 217274
8.3%
n 208865
8.0%
s 170178
 
6.5%
164560
 
6.3%
d 159630
 
6.1%
S 144075
 
5.5%
U 140430
 
5.3%
Other values (52) 370319
14.1%

level1Gid
Text

Missing 

Distinct1804
Distinct (%)0.8%
Missing1694638
Missing (%)88.0%
Memory size14.7 MiB
2025-01-07T10:46:26.325707image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.672701776
Min length6

Characters and Unicode

Total characters1778187
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique305 ?
Unique (%)0.1%

Sample

1st rowUSA.10_1
2nd rowPAN.4_1
3rd rowUSA.14_1
4th rowUSA.16_1
5th rowPAN.12_1
ValueCountFrequency (%)
usa.10_1 18116
 
7.8%
usa.5_1 8182
 
3.5%
usa.43_1 8000
 
3.5%
pan.4_1 7933
 
3.4%
jpn.32_1 6827
 
2.9%
usa.47_1 6423
 
2.8%
usa.21_1 5755
 
2.5%
usa.44_1 5753
 
2.5%
usa.11_1 5094
 
2.2%
usa.9_1 4888
 
2.1%
Other values (1794) 154784
66.8%
2025-01-07T10:46:26.610053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 319208
18.0%
_ 231589
13.0%
. 231553
13.0%
A 166849
9.4%
U 148282
8.3%
S 146784
8.3%
2 66976
 
3.8%
4 61992
 
3.5%
3 50877
 
2.9%
N 36177
 
2.0%
Other values (28) 317900
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1778187
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 319208
18.0%
_ 231589
13.0%
. 231553
13.0%
A 166849
9.4%
U 148282
8.3%
S 146784
8.3%
2 66976
 
3.8%
4 61992
 
3.5%
3 50877
 
2.9%
N 36177
 
2.0%
Other values (28) 317900
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1778187
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 319208
18.0%
_ 231589
13.0%
. 231553
13.0%
A 166849
9.4%
U 148282
8.3%
S 146784
8.3%
2 66976
 
3.8%
4 61992
 
3.5%
3 50877
 
2.9%
N 36177
 
2.0%
Other values (28) 317900
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1778187
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 319208
18.0%
_ 231589
13.0%
. 231553
13.0%
A 166849
9.4%
U 148282
8.3%
S 146784
8.3%
2 66976
 
3.8%
4 61992
 
3.5%
3 50877
 
2.9%
N 36177
 
2.0%
Other values (28) 317900
17.9%

level1Name
Text

Missing 

Distinct1737
Distinct (%)0.7%
Missing1694634
Missing (%)88.0%
Memory size14.7 MiB
2025-01-07T10:46:26.829308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length48
Median length30
Mean length8.96956321
Min length3

Characters and Unicode

Total characters2078777
Distinct characters115
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique296 ?
Unique (%)0.1%

Sample

1st rowFlorida
2nd rowColón
3rd rowIllinois
4th rowIowa
5th rowPanamá
ValueCountFrequency (%)
florida 18120
 
6.1%
california 9283
 
3.1%
carolina 8221
 
2.8%
tennessee 8000
 
2.7%
colón 7933
 
2.7%
virginia 7606
 
2.6%
okinawa 6827
 
2.3%
new 5902
 
2.0%
maryland 5759
 
1.9%
texas 5753
 
1.9%
Other values (1876) 212777
71.8%
2025-01-07T10:46:27.110512image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 294071
14.1%
i 195344
 
9.4%
n 167961
 
8.1%
o 145084
 
7.0%
r 120163
 
5.8%
s 119088
 
5.7%
e 117144
 
5.6%
l 98274
 
4.7%
t 78261
 
3.8%
64422
 
3.1%
Other values (105) 678965
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2078777
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 294071
14.1%
i 195344
 
9.4%
n 167961
 
8.1%
o 145084
 
7.0%
r 120163
 
5.8%
s 119088
 
5.7%
e 117144
 
5.6%
l 98274
 
4.7%
t 78261
 
3.8%
64422
 
3.1%
Other values (105) 678965
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2078777
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 294071
14.1%
i 195344
 
9.4%
n 167961
 
8.1%
o 145084
 
7.0%
r 120163
 
5.8%
s 119088
 
5.7%
e 117144
 
5.6%
l 98274
 
4.7%
t 78261
 
3.8%
64422
 
3.1%
Other values (105) 678965
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2078777
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 294071
14.1%
i 195344
 
9.4%
n 167961
 
8.1%
o 145084
 
7.0%
r 120163
 
5.8%
s 119088
 
5.7%
e 117144
 
5.6%
l 98274
 
4.7%
t 78261
 
3.8%
64422
 
3.1%
Other values (105) 678965
32.7%

level2Gid
Text

Missing 

Distinct7611
Distinct (%)3.5%
Missing1708984
Missing (%)88.7%
Memory size14.7 MiB
2025-01-07T10:46:27.333325image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.36195374
Min length7

Characters and Unicode

Total characters2252782
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1730 ?
Unique (%)0.8%

Sample

1st rowUSA.10.59_1
2nd rowPAN.4.2_1
3rd rowUSA.14.18_1
4th rowUSA.16.3_1
5th rowPAN.12.2_1
ValueCountFrequency (%)
jpn.32.28_1 6059
 
2.8%
usa.10.43_1 6013
 
2.8%
pan.4.2_1 5746
 
2.6%
usa.9.1_1 4888
 
2.2%
usa.10.44_1 4299
 
2.0%
usa.22.1_1 3251
 
1.5%
mdg.2.1_1 2723
 
1.3%
dom.29.3_1 2676
 
1.2%
cri.5.2_1 2210
 
1.0%
pan.4.5_1 2107
 
1.0%
Other values (7601) 177437
81.6%
2025-01-07T10:46:27.614726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 434450
19.3%
1 372514
16.5%
_ 217409
9.7%
A 164597
 
7.3%
U 146942
 
6.5%
S 144683
 
6.4%
2 131222
 
5.8%
4 103045
 
4.6%
3 93136
 
4.1%
5 60231
 
2.7%
Other values (28) 384553
17.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2252782
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 434450
19.3%
1 372514
16.5%
_ 217409
9.7%
A 164597
 
7.3%
U 146942
 
6.5%
S 144683
 
6.4%
2 131222
 
5.8%
4 103045
 
4.6%
3 93136
 
4.1%
5 60231
 
2.7%
Other values (28) 384553
17.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2252782
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 434450
19.3%
1 372514
16.5%
_ 217409
9.7%
A 164597
 
7.3%
U 146942
 
6.5%
S 144683
 
6.4%
2 131222
 
5.8%
4 103045
 
4.6%
3 93136
 
4.1%
5 60231
 
2.7%
Other values (28) 384553
17.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2252782
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 434450
19.3%
1 372514
16.5%
_ 217409
9.7%
A 164597
 
7.3%
U 146942
 
6.5%
S 144683
 
6.4%
2 131222
 
5.8%
4 103045
 
4.6%
3 93136
 
4.1%
5 60231
 
2.7%
Other values (28) 384553
17.1%

level2Name
Text

Missing 

Distinct6184
Distinct (%)2.8%
Missing1709049
Missing (%)88.7%
Memory size14.7 MiB
2025-01-07T10:46:27.841036image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length29
Mean length8.376522931
Min length1

Characters and Unicode

Total characters1820587
Distinct characters147
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1557 ?
Unique (%)0.7%

Sample

1st rowSeminole
2nd rowColón
3rd rowCumberland
4th rowAllamakee
5th rowChepo
ValueCountFrequency (%)
san 6246
 
2.3%
onna 6059
 
2.2%
miami-dade 6013
 
2.2%
colón 5755
 
2.1%
of 5128
 
1.9%
columbia 5068
 
1.8%
monroe 4935
 
1.8%
district 4903
 
1.8%
de 3904
 
1.4%
barnstable 3251
 
1.2%
Other values (6463) 224555
81.4%
2025-01-07T10:46:28.122195image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 214955
 
11.8%
n 155743
 
8.6%
e 142976
 
7.9%
o 137584
 
7.6%
i 116282
 
6.4%
r 99257
 
5.5%
l 83851
 
4.6%
t 80842
 
4.4%
s 71222
 
3.9%
58473
 
3.2%
Other values (137) 659402
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1820587
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 214955
 
11.8%
n 155743
 
8.6%
e 142976
 
7.9%
o 137584
 
7.6%
i 116282
 
6.4%
r 99257
 
5.5%
l 83851
 
4.6%
t 80842
 
4.4%
s 71222
 
3.9%
58473
 
3.2%
Other values (137) 659402
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1820587
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 214955
 
11.8%
n 155743
 
8.6%
e 142976
 
7.9%
o 137584
 
7.6%
i 116282
 
6.4%
r 99257
 
5.5%
l 83851
 
4.6%
t 80842
 
4.4%
s 71222
 
3.9%
58473
 
3.2%
Other values (137) 659402
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1820587
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 214955
 
11.8%
n 155743
 
8.6%
e 142976
 
7.9%
o 137584
 
7.6%
i 116282
 
6.4%
r 99257
 
5.5%
l 83851
 
4.6%
t 80842
 
4.4%
s 71222
 
3.9%
58473
 
3.2%
Other values (137) 659402
36.2%

level3Gid
Text

Missing 

Distinct3021
Distinct (%)7.6%
Missing1886622
Missing (%)97.9%
Memory size14.7 MiB
2025-01-07T10:46:28.340344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length11
Mean length11.67596993
Min length5

Characters and Unicode

Total characters464365
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1211 ?
Unique (%)3.0%

Sample

1st rowPAN.4.2.6_1
2nd rowPAN.12.2.2_1
3rd rowMMR.4.2.6_1
4th rowPAN.12.1.4_1
5th rowCAN.9.20.18_1
ValueCountFrequency (%)
pan.4.2.4_1 3201
 
8.0%
mdg.2.1.5_1 2581
 
6.5%
pan.4.2.6_1 2281
 
5.7%
cri.5.2.1_1 2199
 
5.5%
pan.4.5.5_1 1729
 
4.3%
can.6.2.11_1 743
 
1.9%
pan.11.1.5_1 729
 
1.8%
phl.20.2.8_1 443
 
1.1%
phl.25.27.3_1 382
 
1.0%
pan.12.1.4_1 370
 
0.9%
Other values (3011) 25113
63.1%
2025-01-07T10:46:28.623087image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 119301
25.7%
1 77737
16.7%
_ 39767
 
8.6%
2 30165
 
6.5%
4 20568
 
4.4%
N 19748
 
4.3%
A 19069
 
4.1%
P 17786
 
3.8%
5 17385
 
3.7%
C 11012
 
2.4%
Other values (34) 91827
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 464365
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 119301
25.7%
1 77737
16.7%
_ 39767
 
8.6%
2 30165
 
6.5%
4 20568
 
4.4%
N 19748
 
4.3%
A 19069
 
4.1%
P 17786
 
3.8%
5 17385
 
3.7%
C 11012
 
2.4%
Other values (34) 91827
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 464365
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 119301
25.7%
1 77737
16.7%
_ 39767
 
8.6%
2 30165
 
6.5%
4 20568
 
4.4%
N 19748
 
4.3%
A 19069
 
4.1%
P 17786
 
3.8%
5 17385
 
3.7%
C 11012
 
2.4%
Other values (34) 91827
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 464365
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 119301
25.7%
1 77737
16.7%
_ 39767
 
8.6%
2 30165
 
6.5%
4 20568
 
4.4%
N 19748
 
4.3%
A 19069
 
4.1%
P 17786
 
3.8%
5 17385
 
3.7%
C 11012
 
2.4%
Other values (34) 91827
19.8%

level3Name
Text

Missing 

Distinct2871
Distinct (%)7.4%
Missing1887342
Missing (%)98.0%
Memory size14.7 MiB
2025-01-07T10:46:28.834182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length29
Mean length9.371411744
Min length2

Characters and Unicode

Total characters365963
Distinct characters125
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1139 ?
Unique (%)2.9%

Sample

1st rowCristóbal
2nd rowChepillo
3rd rowMyitkyina
4th rowPedro González
5th rowKenora, Unorganized
ValueCountFrequency (%)
cativá 3201
 
5.8%
nosibe 2581
 
4.7%
cristóbal 2281
 
4.1%
limon 2199
 
4.0%
portobelo 1729
 
3.1%
harbour 745
 
1.4%
sachs 743
 
1.3%
veracruz 729
 
1.3%
santa 615
 
1.1%
unorganized 585
 
1.1%
Other values (3192) 39692
72.0%
2025-01-07T10:46:29.113709image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 46333
 
12.7%
o 27885
 
7.6%
i 25003
 
6.8%
n 22399
 
6.1%
r 19868
 
5.4%
e 19376
 
5.3%
t 17849
 
4.9%
16049
 
4.4%
l 15536
 
4.2%
s 13794
 
3.8%
Other values (115) 141871
38.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 365963
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 46333
 
12.7%
o 27885
 
7.6%
i 25003
 
6.8%
n 22399
 
6.1%
r 19868
 
5.4%
e 19376
 
5.3%
t 17849
 
4.9%
16049
 
4.4%
l 15536
 
4.2%
s 13794
 
3.8%
Other values (115) 141871
38.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 365963
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 46333
 
12.7%
o 27885
 
7.6%
i 25003
 
6.8%
n 22399
 
6.1%
r 19868
 
5.4%
e 19376
 
5.3%
t 17849
 
4.9%
16049
 
4.4%
l 15536
 
4.2%
s 13794
 
3.8%
Other values (115) 141871
38.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 365963
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 46333
 
12.7%
o 27885
 
7.6%
i 25003
 
6.8%
n 22399
 
6.1%
r 19868
 
5.4%
e 19376
 
5.3%
t 17849
 
4.9%
16049
 
4.4%
l 15536
 
4.2%
s 13794
 
3.8%
Other values (115) 141871
38.8%

iucnRedListCategory
Text

Missing 

Distinct13
Distinct (%)< 0.1%
Missing469562
Missing (%)24.4%
Memory size14.7 MiB
2025-01-07T10:46:29.173213image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length2
Mean length2.000048736
Min length2

Characters and Unicode

Total characters2913733
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowNE
2nd rowNE
3rd rowNE
4th rowNE
5th rowNE
ValueCountFrequency (%)
ne 1307916
89.8%
lc 117121
 
8.0%
dd 11259
 
0.8%
nt 6488
 
0.4%
vu 6192
 
0.4%
cr 3404
 
0.2%
en 3150
 
0.2%
ex 1118
 
0.1%
ew 179
 
< 0.1%
2024-12-02t13:57:06.570z 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
2025-01-07T10:46:29.275509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 1317554
45.2%
E 1312363
45.0%
C 120525
 
4.1%
L 117121
 
4.0%
D 22518
 
0.8%
T 6491
 
0.2%
V 6192
 
0.2%
U 6192
 
0.2%
R 3404
 
0.1%
X 1118
 
< 0.1%
Other values (15) 255
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2913733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1317554
45.2%
E 1312363
45.0%
C 120525
 
4.1%
L 117121
 
4.0%
D 22518
 
0.8%
T 6491
 
0.2%
V 6192
 
0.2%
U 6192
 
0.2%
R 3404
 
0.1%
X 1118
 
< 0.1%
Other values (15) 255
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2913733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1317554
45.2%
E 1312363
45.0%
C 120525
 
4.1%
L 117121
 
4.0%
D 22518
 
0.8%
T 6491
 
0.2%
V 6192
 
0.2%
U 6192
 
0.2%
R 3404
 
0.1%
X 1118
 
< 0.1%
Other values (15) 255
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2913733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1317554
45.2%
E 1312363
45.0%
C 120525
 
4.1%
L 117121
 
4.0%
D 22518
 
0.8%
T 6491
 
0.2%
V 6192
 
0.2%
U 6192
 
0.2%
R 3404
 
0.1%
X 1118
 
< 0.1%
Other values (15) 255
 
< 0.1%

Unnamed: 223
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing1926392
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:29.330236image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean1
Median Absolute Deviation (MAD)0
Skewnessnan
Sum1
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:46:29.373588image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1 1
 
< 0.1%
(Missing) 1926392
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%

Unnamed: 224
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing1926392
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean5967481
Minimum5967481
Maximum5967481
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:29.416992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5967481
5-th percentile5967481
Q15967481
median5967481
Q35967481
95-th percentile5967481
Maximum5967481
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean5967481
Median Absolute Deviation (MAD)0
Skewnessnan
Sum5967481
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:46:29.461104image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
5967481 1
 
< 0.1%
(Missing) 1926392
> 99.9%
ValueCountFrequency (%)
5967481 1
< 0.1%
ValueCountFrequency (%)
5967481 1
< 0.1%

Unnamed: 225
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

Unnamed: 226
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

Unnamed: 227
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

Unnamed: 228
Text

Missing 

Distinct3
Distinct (%)100.0%
Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:29.520418image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length115
Median length77
Mean length80
Min length48

Characters and Unicode

Total characters240
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT;CONTINENT_DERIVED_FROM_COUNTRY;CONTINENT_INVALID;TAXON_MATCH_FUZZY
2nd rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT;GEODETIC_DATUM_ASSUMED_WGS84
3rd rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
ValueCountFrequency (%)
occurrence_status_inferred_from_individual_count;continent_derived_from_country;continent_invalid;taxon_match_fuzzy 1
33.3%
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84 1
33.3%
occurrence_status_inferred_from_individual_count 1
33.3%
2025-01-07T10:46:29.643298image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 24
10.0%
N 21
 
8.8%
E 19
 
7.9%
T 18
 
7.5%
R 18
 
7.5%
I 18
 
7.5%
C 17
 
7.1%
U 16
 
6.7%
O 15
 
6.2%
D 15
 
6.2%
Other values (15) 59
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 24
10.0%
N 21
 
8.8%
E 19
 
7.9%
T 18
 
7.5%
R 18
 
7.5%
I 18
 
7.5%
C 17
 
7.1%
U 16
 
6.7%
O 15
 
6.2%
D 15
 
6.2%
Other values (15) 59
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 24
10.0%
N 21
 
8.8%
E 19
 
7.9%
T 18
 
7.5%
R 18
 
7.5%
I 18
 
7.5%
C 17
 
7.1%
U 16
 
6.7%
O 15
 
6.2%
D 15
 
6.2%
Other values (15) 59
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 24
10.0%
N 21
 
8.8%
E 19
 
7.9%
T 18
 
7.5%
R 18
 
7.5%
I 18
 
7.5%
C 17
 
7.1%
U 16
 
6.7%
O 15
 
6.2%
D 15
 
6.2%
Other values (15) 59
24.6%

Unnamed: 229
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

Unnamed: 230
Boolean

Missing 

Distinct2
Distinct (%)66.7%
Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB
False
 
2
True
 
1
(Missing)
1926390 
ValueCountFrequency (%)
False 2
 
< 0.1%
True 1
 
< 0.1%
(Missing) 1926390
> 99.9%
2025-01-07T10:46:29.700702image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Unnamed: 231
Boolean

Constant  Missing 

Distinct1
Distinct (%)33.3%
Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB
False
 
3
(Missing)
1926390 
ValueCountFrequency (%)
False 3
 
< 0.1%
(Missing) 1926390
> 99.9%
2025-01-07T10:46:29.739215image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Unnamed: 232
Unsupported

Missing  Rejected  Unsupported 

Missing1926389
Missing (%)> 99.9%
Memory size14.7 MiB

Unnamed: 233
Real number (ℝ)

Missing 

Distinct3
Distinct (%)100.0%
Missing1926390
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean4921980.333
Minimum2503724
Maximum7029199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:29.779203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2503724
5-th percentile2776653.4
Q13868371
median5233018
Q36131108.5
95-th percentile6849580.9
Maximum7029199
Range4525475
Interquartile range (IQR)2262737.5

Descriptive statistics

Standard deviation2278714.4
Coefficient of variation (CV)0.462966986
Kurtosisnan
Mean4921980.333
Median Absolute Deviation (MAD)1796181
Skewness-0.6027923712
Sum14765941
Variance5.192539316 × 1012
MonotonicityNot monotonic
2025-01-07T10:46:29.824152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
5233018 1
 
< 0.1%
7029199 1
 
< 0.1%
2503724 1
 
< 0.1%
(Missing) 1926390
> 99.9%
ValueCountFrequency (%)
2503724 1
< 0.1%
5233018 1
< 0.1%
7029199 1
< 0.1%
ValueCountFrequency (%)
7029199 1
< 0.1%
5233018 1
< 0.1%
2503724 1
< 0.1%

Unnamed: 234
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)33.3%
Missing1926390
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:29.866177image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosisnan
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum3
Variance0
MonotonicityIncreasing
2025-01-07T10:46:29.911165image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1 3
 
< 0.1%
(Missing) 1926390
> 99.9%
ValueCountFrequency (%)
1 3
< 0.1%
ValueCountFrequency (%)
1 3
< 0.1%

Unnamed: 235
Unsupported

Missing  Rejected  Unsupported 

Missing1926389
Missing (%)> 99.9%
Memory size14.7 MiB

Unnamed: 236
Unsupported

Missing  Rejected  Unsupported 

Missing1926389
Missing (%)> 99.9%
Memory size14.7 MiB

Unnamed: 237
Unsupported

Missing  Rejected  Unsupported 

Missing1926389
Missing (%)> 99.9%
Memory size14.7 MiB

Unnamed: 238
Unsupported

Missing  Rejected  Unsupported 

Missing1926389
Missing (%)> 99.9%
Memory size14.7 MiB

Unnamed: 239
Real number (ℝ)

Missing 

Distinct3
Distinct (%)100.0%
Missing1926390
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean2750679.667
Minimum2499318
Maximum3248997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:29.954675image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2499318
5-th percentile2499758.6
Q12501521
median2503724
Q32876360.5
95-th percentile3174469.7
Maximum3248997
Range749679
Interquartile range (IQR)374839.5

Descriptive statistics

Standard deviation431561.0927
Coefficient of variation (CV)0.1568925302
Kurtosisnan
Mean2750679.667
Median Absolute Deviation (MAD)4406
Skewness1.731847706
Sum8252039
Variance1.862449767 × 1011
MonotonicityNot monotonic
2025-01-07T10:46:30.002199image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2499318 1
 
< 0.1%
3248997 1
 
< 0.1%
2503724 1
 
< 0.1%
(Missing) 1926390
> 99.9%
ValueCountFrequency (%)
2499318 1
< 0.1%
2503724 1
< 0.1%
3248997 1
< 0.1%
ValueCountFrequency (%)
3248997 1
< 0.1%
2503724 1
< 0.1%
2499318 1
< 0.1%

Unnamed: 240
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

Unnamed: 241
Unsupported

Missing  Rejected  Unsupported 

Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB

Unnamed: 242
Text

Missing 

Distinct3
Distinct (%)100.0%
Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:30.048710image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length27
Median length24
Mean length21.33333333
Min length13

Characters and Unicode

Total characters64
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowEchinorhynchus lageniformis
2nd rowNORTH_AMERICA
3rd rowSetaria labiatopapillosa
ValueCountFrequency (%)
echinorhynchus 1
20.0%
lageniformis 1
20.0%
north_america 1
20.0%
setaria 1
20.0%
labiatopapillosa 1
20.0%
2025-01-07T10:46:30.166506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 7
 
10.9%
i 6
 
9.4%
o 4
 
6.2%
l 4
 
6.2%
r 3
 
4.7%
n 3
 
4.7%
h 3
 
4.7%
s 3
 
4.7%
c 2
 
3.1%
E 2
 
3.1%
Other values (21) 27
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7
 
10.9%
i 6
 
9.4%
o 4
 
6.2%
l 4
 
6.2%
r 3
 
4.7%
n 3
 
4.7%
h 3
 
4.7%
s 3
 
4.7%
c 2
 
3.1%
E 2
 
3.1%
Other values (21) 27
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7
 
10.9%
i 6
 
9.4%
o 4
 
6.2%
l 4
 
6.2%
r 3
 
4.7%
n 3
 
4.7%
h 3
 
4.7%
s 3
 
4.7%
c 2
 
3.1%
E 2
 
3.1%
Other values (21) 27
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7
 
10.9%
i 6
 
9.4%
o 4
 
6.2%
l 4
 
6.2%
r 3
 
4.7%
n 3
 
4.7%
h 3
 
4.7%
s 3
 
4.7%
c 2
 
3.1%
E 2
 
3.1%
Other values (21) 27
42.2%

Unnamed: 243
Text

Missing 

Distinct4
Distinct (%)100.0%
Missing1926389
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:30.230972image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length45
Median length31.5
Mean length30.25
Min length13

Characters and Unicode

Total characters121
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowEchinorhynchus lageniformis Ekbaum, 1938
2nd rowNORTH_AMERICA
3rd rowSetaria labiatopapillosa (Alessandrini, 1838)
4th rowSphyranura Wright, 1879
ValueCountFrequency (%)
echinorhynchus 1
8.3%
lageniformis 1
8.3%
ekbaum 1
8.3%
1938 1
8.3%
north_america 1
8.3%
setaria 1
8.3%
labiatopapillosa 1
8.3%
alessandrini 1
8.3%
1838 1
8.3%
sphyranura 1
8.3%
Other values (2) 2
16.7%
2025-01-07T10:46:30.349628image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 11
 
9.1%
i 9
 
7.4%
8
 
6.6%
r 7
 
5.8%
n 6
 
5.0%
h 5
 
4.1%
s 5
 
4.1%
l 5
 
4.1%
8 4
 
3.3%
o 4
 
3.3%
Other values (32) 57
47.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11
 
9.1%
i 9
 
7.4%
8
 
6.6%
r 7
 
5.8%
n 6
 
5.0%
h 5
 
4.1%
s 5
 
4.1%
l 5
 
4.1%
8 4
 
3.3%
o 4
 
3.3%
Other values (32) 57
47.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11
 
9.1%
i 9
 
7.4%
8
 
6.6%
r 7
 
5.8%
n 6
 
5.0%
h 5
 
4.1%
s 5
 
4.1%
l 5
 
4.1%
8 4
 
3.3%
o 4
 
3.3%
Other values (32) 57
47.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11
 
9.1%
i 9
 
7.4%
8
 
6.6%
r 7
 
5.8%
n 6
 
5.0%
h 5
 
4.1%
s 5
 
4.1%
l 5
 
4.1%
8 4
 
3.3%
o 4
 
3.3%
Other values (32) 57
47.1%

Unnamed: 244
Text

Missing 

Distinct3
Distinct (%)100.0%
Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:30.403618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length26
Median length24
Mean length21.33333333
Min length14

Characters and Unicode

Total characters64
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowEchinorhynchus lageniforme
2nd rowSetaria labiatopapillosa
3rd rowSphyranura sp.
ValueCountFrequency (%)
echinorhynchus 1
16.7%
lageniforme 1
16.7%
setaria 1
16.7%
labiatopapillosa 1
16.7%
sphyranura 1
16.7%
sp 1
16.7%
2025-01-07T10:46:30.512941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 9
14.1%
i 5
 
7.8%
r 5
 
7.8%
o 4
 
6.2%
n 4
 
6.2%
h 4
 
6.2%
l 4
 
6.2%
p 4
 
6.2%
3
 
4.7%
s 3
 
4.7%
Other values (12) 19
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 9
14.1%
i 5
 
7.8%
r 5
 
7.8%
o 4
 
6.2%
n 4
 
6.2%
h 4
 
6.2%
l 4
 
6.2%
p 4
 
6.2%
3
 
4.7%
s 3
 
4.7%
Other values (12) 19
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 9
14.1%
i 5
 
7.8%
r 5
 
7.8%
o 4
 
6.2%
n 4
 
6.2%
h 4
 
6.2%
l 4
 
6.2%
p 4
 
6.2%
3
 
4.7%
s 3
 
4.7%
Other values (12) 19
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 9
14.1%
i 5
 
7.8%
r 5
 
7.8%
o 4
 
6.2%
n 4
 
6.2%
h 4
 
6.2%
l 4
 
6.2%
p 4
 
6.2%
3
 
4.7%
s 3
 
4.7%
Other values (12) 19
29.7%

Unnamed: 245
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

Unnamed: 246
Text

Constant  Missing 

Distinct1
Distinct (%)33.3%
Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:30.556445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEML
2nd rowEML
3rd rowEML
ValueCountFrequency (%)
eml 3
100.0%
2025-01-07T10:46:30.647355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 3
33.3%
M 3
33.3%
L 3
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 3
33.3%
M 3
33.3%
L 3
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 3
33.3%
M 3
33.3%
L 3
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 3
33.3%
M 3
33.3%
L 3
33.3%

Unnamed: 247
Text

Missing 

Distinct3
Distinct (%)100.0%
Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:30.703049image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters72
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row2024-12-02T13:57:06.570Z
2nd row2024-12-02T13:57:15.729Z
3rd row2024-12-02T13:57:15.950Z
ValueCountFrequency (%)
2024-12-02t13:57:06.570z 1
33.3%
2024-12-02t13:57:15.729z 1
33.3%
2024-12-02t13:57:15.950z 1
33.3%
2025-01-07T10:46:30.810240image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 13
18.1%
0 9
12.5%
1 8
11.1%
5 7
9.7%
: 6
8.3%
- 6
8.3%
7 5
 
6.9%
4 3
 
4.2%
T 3
 
4.2%
3 3
 
4.2%
Other values (4) 9
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 13
18.1%
0 9
12.5%
1 8
11.1%
5 7
9.7%
: 6
8.3%
- 6
8.3%
7 5
 
6.9%
4 3
 
4.2%
T 3
 
4.2%
3 3
 
4.2%
Other values (4) 9
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 13
18.1%
0 9
12.5%
1 8
11.1%
5 7
9.7%
: 6
8.3%
- 6
8.3%
7 5
 
6.9%
4 3
 
4.2%
T 3
 
4.2%
3 3
 
4.2%
Other values (4) 9
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 13
18.1%
0 9
12.5%
1 8
11.1%
5 7
9.7%
: 6
8.3%
- 6
8.3%
7 5
 
6.9%
4 3
 
4.2%
T 3
 
4.2%
3 3
 
4.2%
Other values (4) 9
12.5%

Unnamed: 248
Text

Constant  Missing 

Distinct1
Distinct (%)33.3%
Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:30.860792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters72
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-12-02T11:48:23.416Z
2nd row2024-12-02T11:48:23.416Z
3rd row2024-12-02T11:48:23.416Z
ValueCountFrequency (%)
2024-12-02t11:48:23.416z 3
100.0%
2025-01-07T10:46:31.054070image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 15
20.8%
1 12
16.7%
4 9
12.5%
0 6
 
8.3%
- 6
 
8.3%
: 6
 
8.3%
T 3
 
4.2%
8 3
 
4.2%
3 3
 
4.2%
. 3
 
4.2%
Other values (2) 6
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 15
20.8%
1 12
16.7%
4 9
12.5%
0 6
 
8.3%
- 6
 
8.3%
: 6
 
8.3%
T 3
 
4.2%
8 3
 
4.2%
3 3
 
4.2%
. 3
 
4.2%
Other values (2) 6
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 15
20.8%
1 12
16.7%
4 9
12.5%
0 6
 
8.3%
- 6
 
8.3%
: 6
 
8.3%
T 3
 
4.2%
8 3
 
4.2%
3 3
 
4.2%
. 3
 
4.2%
Other values (2) 6
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 15
20.8%
1 12
16.7%
4 9
12.5%
0 6
 
8.3%
- 6
 
8.3%
: 6
 
8.3%
T 3
 
4.2%
8 3
 
4.2%
3 3
 
4.2%
. 3
 
4.2%
Other values (2) 6
 
8.3%

Unnamed: 249
Boolean

Missing 

Distinct2
Distinct (%)66.7%
Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB
False
 
2
True
 
1
(Missing)
1926390 
ValueCountFrequency (%)
False 2
 
< 0.1%
True 1
 
< 0.1%
(Missing) 1926390
> 99.9%
2025-01-07T10:46:31.109633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Unnamed: 250
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

Unnamed: 251
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

Unnamed: 252
Boolean

Constant  Missing 

Distinct1
Distinct (%)33.3%
Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB
False
 
3
(Missing)
1926390 
ValueCountFrequency (%)
False 3
 
< 0.1%
(Missing) 1926390
> 99.9%
2025-01-07T10:46:31.148139image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Unnamed: 253
Text

Constant  Missing 

Distinct1
Distinct (%)33.3%
Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:31.178139image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters39
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTH_AMERICA
2nd rowNORTH_AMERICA
3rd rowNORTH_AMERICA
ValueCountFrequency (%)
north_america 3
100.0%
2025-01-07T10:46:31.281333image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 6
15.4%
A 6
15.4%
N 3
7.7%
O 3
7.7%
T 3
7.7%
H 3
7.7%
_ 3
7.7%
M 3
7.7%
E 3
7.7%
I 3
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 6
15.4%
A 6
15.4%
N 3
7.7%
O 3
7.7%
T 3
7.7%
H 3
7.7%
_ 3
7.7%
M 3
7.7%
E 3
7.7%
I 3
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 6
15.4%
A 6
15.4%
N 3
7.7%
O 3
7.7%
T 3
7.7%
H 3
7.7%
_ 3
7.7%
M 3
7.7%
E 3
7.7%
I 3
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 6
15.4%
A 6
15.4%
N 3
7.7%
O 3
7.7%
T 3
7.7%
H 3
7.7%
_ 3
7.7%
M 3
7.7%
E 3
7.7%
I 3
7.7%

Unnamed: 254
Text

Constant  Missing 

Distinct1
Distinct (%)33.3%
Missing1926390
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:31.333843image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters39
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTH_AMERICA
2nd rowNORTH_AMERICA
3rd rowNORTH_AMERICA
ValueCountFrequency (%)
north_america 3
100.0%
2025-01-07T10:46:31.431467image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 6
15.4%
A 6
15.4%
N 3
7.7%
O 3
7.7%
T 3
7.7%
H 3
7.7%
_ 3
7.7%
M 3
7.7%
E 3
7.7%
I 3
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 6
15.4%
A 6
15.4%
N 3
7.7%
O 3
7.7%
T 3
7.7%
H 3
7.7%
_ 3
7.7%
M 3
7.7%
E 3
7.7%
I 3
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 6
15.4%
A 6
15.4%
N 3
7.7%
O 3
7.7%
T 3
7.7%
H 3
7.7%
_ 3
7.7%
M 3
7.7%
E 3
7.7%
I 3
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 6
15.4%
A 6
15.4%
N 3
7.7%
O 3
7.7%
T 3
7.7%
H 3
7.7%
_ 3
7.7%
M 3
7.7%
E 3
7.7%
I 3
7.7%

Unnamed: 255
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing1926392
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:31.473469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowUSA
ValueCountFrequency (%)
usa 1
100.0%
2025-01-07T10:46:31.565371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 1
33.3%
S 1
33.3%
A 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 1
33.3%
S 1
33.3%
A 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 1
33.3%
S 1
33.3%
A 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 1
33.3%
S 1
33.3%
A 1
33.3%

Unnamed: 256
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing1926392
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:31.606989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters13
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowUnited States
ValueCountFrequency (%)
united 1
50.0%
states 1
50.0%
2025-01-07T10:46:31.703093image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 3
23.1%
e 2
15.4%
n 1
 
7.7%
U 1
 
7.7%
i 1
 
7.7%
d 1
 
7.7%
1
 
7.7%
S 1
 
7.7%
a 1
 
7.7%
s 1
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 3
23.1%
e 2
15.4%
n 1
 
7.7%
U 1
 
7.7%
i 1
 
7.7%
d 1
 
7.7%
1
 
7.7%
S 1
 
7.7%
a 1
 
7.7%
s 1
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 3
23.1%
e 2
15.4%
n 1
 
7.7%
U 1
 
7.7%
i 1
 
7.7%
d 1
 
7.7%
1
 
7.7%
S 1
 
7.7%
a 1
 
7.7%
s 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 3
23.1%
e 2
15.4%
n 1
 
7.7%
U 1
 
7.7%
i 1
 
7.7%
d 1
 
7.7%
1
 
7.7%
S 1
 
7.7%
a 1
 
7.7%
s 1
 
7.7%

Unnamed: 257
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing1926392
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:31.745600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowUSA.11_1
ValueCountFrequency (%)
usa.11_1 1
100.0%
2025-01-07T10:46:31.841095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3
37.5%
U 1
 
12.5%
S 1
 
12.5%
A 1
 
12.5%
. 1
 
12.5%
_ 1
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3
37.5%
U 1
 
12.5%
S 1
 
12.5%
A 1
 
12.5%
. 1
 
12.5%
_ 1
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3
37.5%
U 1
 
12.5%
S 1
 
12.5%
A 1
 
12.5%
. 1
 
12.5%
_ 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3
37.5%
U 1
 
12.5%
S 1
 
12.5%
A 1
 
12.5%
. 1
 
12.5%
_ 1
 
12.5%

Unnamed: 258
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing1926392
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:31.884387image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowGeorgia
ValueCountFrequency (%)
georgia 1
100.0%
2025-01-07T10:46:31.980572image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 1
14.3%
e 1
14.3%
o 1
14.3%
r 1
14.3%
g 1
14.3%
i 1
14.3%
a 1
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 1
14.3%
e 1
14.3%
o 1
14.3%
r 1
14.3%
g 1
14.3%
i 1
14.3%
a 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 1
14.3%
e 1
14.3%
o 1
14.3%
r 1
14.3%
g 1
14.3%
i 1
14.3%
a 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 1
14.3%
e 1
14.3%
o 1
14.3%
r 1
14.3%
g 1
14.3%
i 1
14.3%
a 1
14.3%

Unnamed: 259
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing1926392
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:32.025574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters11
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowUSA.11.35_1
ValueCountFrequency (%)
usa.11.35_1 1
100.0%
2025-01-07T10:46:32.123072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3
27.3%
. 2
18.2%
S 1
 
9.1%
U 1
 
9.1%
A 1
 
9.1%
3 1
 
9.1%
5 1
 
9.1%
_ 1
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3
27.3%
. 2
18.2%
S 1
 
9.1%
U 1
 
9.1%
A 1
 
9.1%
3 1
 
9.1%
5 1
 
9.1%
_ 1
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3
27.3%
. 2
18.2%
S 1
 
9.1%
U 1
 
9.1%
A 1
 
9.1%
3 1
 
9.1%
5 1
 
9.1%
_ 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3
27.3%
. 2
18.2%
S 1
 
9.1%
U 1
 
9.1%
A 1
 
9.1%
3 1
 
9.1%
5 1
 
9.1%
_ 1
 
9.1%

Unnamed: 260
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing1926392
Missing (%)> 99.9%
Memory size14.7 MiB
2025-01-07T10:46:32.165145image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowColquitt
ValueCountFrequency (%)
colquitt 1
100.0%
2025-01-07T10:46:32.257927image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 2
25.0%
o 1
12.5%
C 1
12.5%
l 1
12.5%
q 1
12.5%
u 1
12.5%
i 1
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 2
25.0%
o 1
12.5%
C 1
12.5%
l 1
12.5%
q 1
12.5%
u 1
12.5%
i 1
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 2
25.0%
o 1
12.5%
C 1
12.5%
l 1
12.5%
q 1
12.5%
u 1
12.5%
i 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 2
25.0%
o 1
12.5%
C 1
12.5%
l 1
12.5%
q 1
12.5%
u 1
12.5%
i 1
12.5%

Unnamed: 261
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB

Unnamed: 262
Unsupported

Missing  Rejected  Unsupported 

Missing1926393
Missing (%)100.0%
Memory size14.7 MiB


Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.7 MiB
2025-01-07T10:46:32.300694image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length1
Mean length1.000002076
Min length1

Characters and Unicode

Total characters1926397
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
ne 2
100.0%
2025-01-07T10:46:32.409007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1926393
> 99.9%
N 2
 
< 0.1%
E 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1926397
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1926393
> 99.9%
N 2
 
< 0.1%
E 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1926397
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1926393
> 99.9%
N 2
 
< 0.1%
E 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1926397
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1926393
> 99.9%
N 2
 
< 0.1%
E 2
 
< 0.1%